Accelerating preventative maintenance recovery and recipe optimizing using machine-learning based algorithm

ABSTRACT

A method for determining processing chamber conditions using sensor data and a machine learning model is provided. The method includes receiving, by a processing device, sensor data that include chamber data indicating a state of an environment of a processing chamber processing a substrate according to a set of process parameters of a current process. The sensor data further include spectral data indicating optical emission spectra (OES) measurements of a plasma disposed within the processing chamber. The method further includes using the sensor data as input to a machine learning model and obtaining one or more outputs that indicate one or more chamber condition metrics. The method further includes determining a recovery status of a processing chamber based on the one or more chamber condition metrics. The method further includes causing a modification to a performance of the processing chamber based on the recovery status of the processing chamber.

TECHNICAL FIELD

Embodiments of the present disclosure relate, in general, to predictingchamber conditions of manufacturing systems. Specifically, the presentdisclosure relates to determining chamber conditions to identify chamberrecover status and/or update parameters of a process.

BACKGROUND

Substrate processing may include a series of processes that produceelectrical circuits in a semiconductor, e.g., a silicon wafer, inaccordance with a circuit design. These processes may be carried out ina series of chambers. Successful operation of a modern semiconductorfabrication facility may aim to facilitate a steady stream of wafers tobe moved from one chamber to another in the course of forming electricalcircuits in the wafer. In the process of performing many substrateprocesses, conditions of processing chambers may be altered and mayresult processed substrate failing to meet desired conditions andoutcomes.

One such substrate process may include plasma etching, which is aprocess of transferring a pattern in a layer of mask material intoanother layer under the mask, such as a layer of conductive ordielectric material, by removing the layered material from the wafersurface. Such a process inevitably generates different kinds of etchby-products, such as silicon oxide and organic polymer, depending on thelayered material and the etch chemistry. Some of the by-products depositonto interior surfaces of the chamber in which the plasma etchingprocess is performed. The deposition of the by-products may affect etchperformance such as by depositing particles (e.g., flakes) onto thesubstrate or by reacting with the plasma and affecting the processresult.

To mitigate the impact of etch by-products, preventative maintenancesuch as chamber cleaning may be employed to periodically remove thedeposition from the chamber wall. An example of preventative maintenancemay include taking the chamber out of production and introducing acleaning plasma, such as a CF₄+O₂ plasma for cleaning silicon oxidedeposited during silicon etching, into the chamber. This plasma reactswith the deposited material and the products of this reaction are pumpedout of the chamber. After such chamber cleaning, however, it has beenobserved that a clean chamber wall make the chamber unsuitable forimmediate production wafer etching. Chamber seasoning is a procedure ofetching a series of substrates (e.g., blank silicon wafers) to restore achamber condition that is suitable for production substrate processing.After chamber seasoning, a thin layer of silicon oxide may cover thechamber wall. The chamber is then returned to production wafer etchinguntil the next round of chamber cleaning and seasoning. Preventativemaintenance may also include removing dirt and/or deposition by physicalmethods (e.g., wiping off one or more surfaces of the process chamber).

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In an exemplary embodiment, a method includes a processing devicereceiving sensor data that includes chamber data indicating a state ofan environment of a processing chamber processing a substrate accordingto a set of process parameters of a current process. The chamber datafurther includes spectral data indicating optical emission spectra (OES)measurement of plasma disposed within the process chamber. Theprocessing chamber processes the substrate according to the set ofprocess parameters of the current process. The method further includesusing the sensor data as input to a machine learning model. The methodfurther includes obtaining one or more outputs of the machine learningmodel. The one or more outputs indicate one or more chamber conditionmetrics. The method further includes determining a recovery status ofthe processing chamber based on the one or more chamber conditionmetrics, the recovery status associated with a chamber recovery process(e.g., chamber seasoning procedure) performed subsequent to apreventative maintenance procedure. The method further includes causinga modification to a performance of the processing chamber based on therecovery status of the processing chamber.

In an exemplary embodiment, a method for training a machine learningmodel to determine a status of a processing chamber in a chamberrecovery procedure is provided. The processing chamber processes acurrent substrate according to a current process. The method includesgenerating training data for the machine learning model. Generating thetraining data includes identifying a first training input havinghistorical sensor data including historical chamber data. The historicalchamber data indicates a state of an environment of a second processingchamber processing a prior substrate according to a prior process. Thesensor data further includes historical spectral data indicating opticalemission spectra (OES) measurements of a prior plasma disposed withinthe second processing chamber processing the prior substrate accordingto the prior process. Generating the training data further includesidentifying a first target output for the first training input. Thefirst target output includes historical process result data havingprocess result measurement of the prior substrate processed using thesecond processing chamber according to the prior process. The methodfurther includes providing the training data to train the machinelearning model on a set of training inputs comprising the first traininginput and a set of target outputs comprising the first target output.

In an exemplary embodiment, a non-transitory computer readable mediumcomprising instruction that, when executed by a processing device causethe processing device to perform actions. The performed actions includereceiving sensor data that includes chamber data indicating a state ofan environment of a processing chamber processing a substrate accordingto a set of process parameters of a current process. The chamber datafurther includes spectral data indicating optical emission spectra (OES)measurement of plasma disposed within the process chamber. Theprocessing chamber processes the substrate according to the set ofprocess parameters of the current process. The actions further includeusing the sensor data as input to a machine learning model. The methodfurther includes obtaining one or more outputs of the machine learningmodel. The one or more outputs indicating one or more chamber conditionsmetrics. The actions further include determining a recovery status ofthe processing chamber based on the one or more chamber conditionmetrics, the recovery status associated with a chamber recovery processperformed subsequent to a preventative maintenance procedure. Theactions further include causing a modification to a performance of theprocessing chamber based on the recovery status of the processingchamber.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that differentreferences to “an” or “one” embodiment in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone.

FIG. 1 depicts an illustrative system architecture, according to aspectsof the present disclosure.

FIG. 2 is a top schematic view of an example manufacturing system,according to aspects of the present disclosure.

FIG. 3 depicts an illustrative system architecture for chamber conditionprediction of a processing chamber, according to aspects of the presentdisclosure.

FIG. 4 is a flow chart of a method for training a machine learningmodel, according to aspects of the present disclosure.

FIG. 5 is a cross-sectional schematic side view of chamber statusequipment, according to aspects of the present disclosure.

FIG. 6 is a flow chart of a method for training and/or updating amachine learning model for predicting chamber condition of a processingchamber, according to aspects of the present disclosure.

FIG. 7 is a flow chart of a method for training and/or updating amachine learning model for predicting chamber condition of a processingchamber, according to aspects of the present disclosure.

FIG. 8 is a flow chart of a method for predicting chamber conditions ofa processing chamber processing a current chamber using a machinelearning model, according to aspects of the present disclosure.

FIG. 9 is a flow chart of a method for selecting a machine learningmodel for estimating a type of metrology measurement value, according toaspects of the present disclosure.

FIG. 10 depicts a diagrammatic representation of a machine in theexample form of a computing device within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, can be executed.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure are directed to a spectralmeasurement system for enhanced spectral data collection and predictingchamber condition metrics using machine learning. Process results ofmanufacturing processes depend on many factors, one of which is aprocess recipe and/or one or more chamber parameter settings. Anotheroften occurring factor is process shift resulting from PreventativeMaintenance (PM) or other events, which causes process result changeseven if recipes remain the same. A newly-serviced chamber (post-PM) orcold chamber often has a “first-wafer-effect” where process results ofsubstrates processed using the newly-serviced or cold chamber deviatefrom process results of substrates processed by chambers that are notrecently serviced. The effects of PM can be remedied by performing oneor a few seasoning wafer runs to season the chamber to its ‘normal’operation condition. In some cases and some applications, post-PMchambers take a few days/weeks to reach their desired ‘normal’ operationconditions.

Preventative maintenance procedures (e.g., chamber cleaning) are oftenused as part of a chamber recovery process to return a state of theprocessing chamber to a state suitable for entering a substrateprocessing production mode (e.g., mass processing of substrates). Arecovery procedure is often used subsequent to a preventativemaintenance procedure to prepare a chamber for a production mode (e.g.,“warm up” the chamber). A common recovery procedure conventionallyemployed is seasoning a processing chamber. Chamber seasoning is aprocedure that includes processing a series of substrates (e.g., blanksilicon wafers) to restore a chamber condition (e.g., coating the wallsof the chamber) that is suitable for a production substrate process(e.g., substrates processed in the chamber having process results thatmeet desired threshold criteria). After chamber seasoning, a chamber mayoperate in a production mode for a period of time until another round ofpreventative maintenance and further chamber seasoning is performed orotherwise recommended to restore a state of the processing chamber.

The penalty of ‘abnormal’ chamber conditions can include failure for asubstrate process to produce a substrate meeting target process results(e.g., film depth, critical dimension on a wafer, side wall angle,etc.). For example, a process recipe may indicate a processing time andan “abnormal” condition would prevent the substrate from meeting atarget process result in the indicated recipe time. Chamberabnormalities can cause process/etch result deviations from a targetprocess result across a batch of substrates. For example, abnormalchamber conditions may cause a processed substrate to have an etchpattern that deviates from a target etch pattern. Failure to achieve atarget etch pattern on the surface of the substrates can cause a yielddecrease of the manufactured integrated circuit (IC) chips ultimatelyproduced from the substrate.

Knowing the current chamber condition (e.g., chamber condition metrics)of a chamber can allow a fabrication system to adaptively revise processparameters to achieve target process results. However, there isdifficulty in identifying chamber conditions. The difficulty is due inpart to subtle differences in chamber parameters between ‘normal’ and‘abnormal’ conditions. For example, there may be some differences inoptical emission spectroscopy (OES) spectra after a chamber has recentlyundergone preventative maintenance (PM), however, predicting the effectsof these subtle difference on process result remains complicated.

Conventionally, a series of substrates are processed within a processingchamber subsequent to a preventative maintenance procedure. Subsequentto processing the series of substrates a test sample is processed andevaluated. A test sample may include a substrate that is processed inthe processing chamber and is evaluated (e.g., process results aremeasured) to determine whether the test sample contains process resultsmeeting threshold conditions. If the condition(s) are not met furtherseasoning substrate may be processed and another test sample may beprocessed to determine whether a chamber is fully recovered. Thisprocess may be repeated until a chamber condition is met or a thresholdquantity of seasoning wavers have been used. Conventional methods ofdetermining chamber conditions, such as previously described, canrequire unnecessary quantities of seasoning wafers (e.g.,“over-seasoning” a chamber) when a first quantity of seasoning wafer areused but a smaller number is actually required to recover a chamber intoan operable state to carry out a substrate production. Additionally,conventional methods can necessitate delays in returning a chamber to aproduction mode by waiting for test sample measurements and evaluationsneeded to determine whether a chamber has sufficiently recovered. Thetime taking for the recovery procedures is a loss in productivitybecause a tool or chamber is down (e.g., not operating in a productionmode). Furthermore, conventional methods do not provide for in-situchamber condition monitoring but depend on test sample and awaiting testsample evaluations during a seasoning process.

Aspects and implementations of the present disclosure address these andother shortcomings of conventional technology by leveraging machinelearning to identify chamber condition metrics associated with processresults of substrates processed within a chamber. The application ofMachine-learning and deep-learning provide a way to identify the uniquefeatures in data and build correlation models between data and chamberlabels. Using a trained model, a processing chamber can provide in-situspectral data that can be used for real time chamber conditionprediction and control. This may be employed, for example, to achieveconsistent process results even in the presence of diverse chamberconditions. The chamber conditions can be mapped to a status of aprocessing chamber on a chamber recovery process. For example,preventative maintenance may be performed on a chamber and a chamber mayundergo a recovery process (e.g., a chamber seasoning procedure) toreturn the chamber to a consistent operational state. Different stagesof the recovery process may result to different chamber conditions suchthat a chamber condition may be used to indicate which stage of therecovery process a processing chamber is currently operating under.

Aspects and implementations of the present disclosure address these andother shortcomings of the existing technology by providing methods andsystems in various embodiments capable of estimating chamber conditionswithout depending on inspection results of test samples, providingquantitative information regarding chamber health and recoverysubsequent to a preventive maintenance procedure, and enable simulationof chamber conditions with artificial parameter adjustments. In someembodiments, the present disclosure enables health check and precautionduring a chamber seasoning sequence and early detection of abnormalchamber condition before a sample test. One of more of these aspectsand/or implementations may result in reduced down time of processingtools (e.g., processing chambers) and can enable greater productivity ofthe manufacturing equipment. In addition, it may be possible to reviseprocess parameters based on the ML model using chamber sensor or OpticalEmission Spectroscopy (OES). The revisions of the process parameter maypermit a chamber to return to production much faster without waiting forfull seasoning and full verification of chamber status by metrologymeasurement.

Optical Emission Spectroscopy (OES), reflectometer spectra (e.g.,endpoint detection interferometric (IEP)), and chamber sensor data(e.g., temperature, pressure, radio frequency (RF) power, plasmaconditions, vacuum conditions, etc.) can be indicators of chamberconditions. Chamber conditions change after PM, when it is cold, andfurther after the chamber is seasoned. These changes cause processresults (e.g., etch results) to vary even when the same recipe is used.The varying process results (e.g., yield, electrical measurement,metrology data, etc.) can be processed to identify proper labels formodel training. Chamber condition labels (e.g., chamber conditionmetrics) with associated OES and chamber sensor data can be used astraining data for a Machine-Learning model. In some embodiments, themachine-learning type may include a Neural Network, and/or may includelogistic regression and multi-layer perceptron. After a model istrained, validated and tested, it may be applied in a prediction engine.The engine takes in-situ OES spectra and sensor data, and predictschamber conditions. It can further revise/suggest optimum chamberparameters for current and/or future wafer processing.

The process results (e.g., process yield, electrical measurement,metrology data, etc.) for a substrate (e.g., from production lots) canbe leveraged to examine model performance. When results are notsatisfactory, chambers can be inspected, tested, and improved. Furthertraining can be conducted when results are not satisfactory or when newchamber conditions are identified/found in new lots run (e.g., toaccommodate more complete chamber conditions).

In an exemplary embodiment, a method includes a processing devicereceiving sensor data that includes chamber data indicating a state ofan environment of a processing chamber processing a substrate accordingto a set of process parameters of a current process. The chamber datafurther includes spectral data indicating optical emission spectra (OES)measurement of plasma disposed within the process chamber. Theprocessing chamber processes the substrate according to the set ofprocess parameters of the current process. The method further includesusing the sensor data as input to a machine learning model. The methodfurther includes obtaining one or more outputs of the machine learningmodel. The one or more outputs indicating one or more chamber conditionmetrics. The method further includes determining a recovery status ofthe processing chamber based on the one or more chamber conditionmetrics, the recovery status associated with a chamber recovery processperformed subsequent to a preventative maintenance procedure. The methodfurther includes causing a modification to a performance of theprocessing chamber based on the recovery status of the processingchamber. For example, processing parameters for a current substrate or afuture substrate may be processed or further processed according to themodification of the processing chamber.

In an exemplary embodiment, a method for training a machine learningmodel to determine a status of a processing chamber in a chamberrecovery procedure is provided. The method includes generating trainingdata for the machine learning model. Generating the training dataincludes identifying a first training input having historical sensordata including historical chamber data. The historical chamber dataindicates a state of an environment of a second processing chamberprocessing a prior substrate according to a prior process. The sensordata further includes historical spectral data indicating opticalemission spectra (OES) measurement of a prior plasma disposed within thesecond processing chamber processing the prior substrate according tothe prior process. Generating the training data further includesidentifying a first target output for the first training input. Thefirst target output includes historical process result data havingprocess result measurements of the prior substrate processed using thesecond processing chamber according to the prior process. The methodfurther includes providing the training data to train the machinelearning model on a set of training inputs comprising the first traininginput and a set of target outputs comprising the first target output.

In some embodiments the trained machine learning model is to receive anew input having new sensor data having new chamber data indicating anew state of a new environment of a new processing chamber processing anew substrate according to a new process and new spectral dataindicating optical emission spectra (OES) measurements of a new plasmadisposed within the new processing chamber processing the new substrateaccording to the new process to produce a new output based on the newinput. The new output indicates a chamber condition metric correspondingto a recovery status associated with a chamber recovery processperformed subsequent to a preventative maintenance procedure.

In an exemplary embodiment, a non-transitory computer readable mediumcomprises instruction that, when executed by a processing device, causethe processing device to perform actions. The performed actions includereceiving sensor data that includes chamber data indicating a state ofan environment of a processing chamber processing a substrate accordingto a set of process parameters of a current process. For example, theprocessing chamber may be processing one or more seasoning wafers as apart of a chamber recovery process. The chamber data further includesspectral data indicating optical emission spectra (OES) measurements ofplasma disposed within the process chamber. The processing chamberprocesses the substrate according to the set of process parameters ofthe current process. The actions further include using the sensor dataas input to a machine learning model. The method further includesobtaining one or more outputs of the machine learning model. The one ormore outputs indicate one or more chamber condition metrics. The actionsfurther include determining a recovery status of the processing chamberbased on the one or more chamber condition metrics, where the recoverystatus is associated with a chamber recovery process (e.g., chamberseasoning procedure) performed subsequent to a preventative maintenanceprocedure. The actions further include causing a modification to aperformance of the processing chamber based on the recovery status ofthe processing chamber. For example, the modification may be associatedwith a current substrate process such as processing a current substrateaccording to updated process parameters. Process parameters may includetemperature, gas flow, gas pressure, electrical power, electrical bias,and so on. In another example, the modification may include halting acurrent substrate process. In another example, the modification mayinclude a change to a process recipe used to process a future substrate.

FIG. 1 depicts an illustrative system architecture 100, according toaspects of the present disclosure. System architecture 100 includes aclient device 120, manufacturing equipment 122, metrology equipment 132,a predictive server 112 (e.g., to generate predictive data, to providemodel adaptation, to use a knowledge base, etc.), and a data store 150.The predictive server 112 can be part of a predictive system 110. Thepredictive system 110 can further include server machines 170 and 180.In some embodiments, system architecture 100 can include or be a part ofa manufacturing system for processing substrates, such as manufacturingsystem 200 of FIG. 2 . In additional or alternative embodiments, systemarchitecture 100 can include or be a part of a chamber conditionprediction system (e.g., determining a recovery status of a chamber froma PM procedure). Further details regarding the chamber conditionprediction system are provided with respect to FIG. 3 .

Components of the client device 120, manufacturing equipment 122,metrology equipment 132, predictive system 110, and/or data store 150can be coupled to each other via a network 140. In some embodiments,network 140 is a public network that provides client device 120 withaccess to predictive server 112, data store 150, and other publiclyavailable computing devices. In some embodiments, network 140 is aprivate network that provides client device 120 access to manufacturingequipment 122, metrology equipment 132, data store 150, and otherprivately available computing devices. Network 140 can include one ormore wide area networks (WANs), local area networks (LANs), wirednetworks (e.g., Ethernet network), wireless networks (e.g., an 802.11network or a Wi-Fi network), cellular networks (e.g., a Long TermEvolution (LTE) network), routers, hubs, switches, server computers,cloud computing networks, and/or a combination thereof.

The client device 120 can include a computing device such as personalcomputers (PCs), laptops, mobile phones, smart phones, tablet computers,netbook computers, network connected televisions (“smart TVs”),network-connected media players (e.g., Blu-ray player), a set-top box,over-the-top (OTT) streaming devices, operator boxes, etc.

Manufacturing equipment 122 can produce products following a recipe orperforming runs over a period of time. In some embodiments,manufacturing equipment 122 can include or be a part of a process toolthat includes one or more stations (e.g., process chamber, transferchamber, load lock, etc.) configured to perform a different function fora substrate. In some embodiments, manufacturing equipment 122 canfurther include chamber status equipment 124 that is configured tocollect data to be used for determining chamber conditions (e.g.,chamber condition metrics) of a processing chamber performing a processfor a substrate at manufacturing equipment 122. A condition of a chambermay refer to a status of a processing chamber in a recovery process(e.g., chamber seasoning procedure) subsequent to a PM procedure beingperformed. Chamber status equipment 124 can include one or morecomponents configured to collect and/or generate spectral data (e.g.,OES or reflectometry spectra) associated with one or more portions of aprofile of a surface of the substrate during a substrate process.Spectral data refers to data associated with an intensity (i.e., astrength or amount of energy) for a detected wave of energy for eachwavelength of the detected wave.

In some embodiments, chamber status equipment 124 can include an opticalfiber bundle and a collimator assembly that are configured to directincident light from a light source to a surface of a substrate andtransmit reflected light from the substrate surface to a light detectioncomponent. A processing device (e.g., a system controller for theprocess tool) coupled to chamber status equipment 124 can generate thespectral data for the substrate profile based on the reflected lighttransmitted to the light detection component and/or spectral dataassociated with an OES of the plasma emitted within the processingchamber. In other or similar embodiments, chamber status equipment 124can include any sensors configured to generate spectral data associatedwith the substrate profile. Such sensors can include reflectometrysensors, ellipsometry sensors, thermal spectra sensors, capacitivesensors, and so forth. Further details regarding manufacturing equipment122 and chamber status equipment 124 are described with regard to FIGS.2 and 5 , respectively.

In some embodiments, one or more stations of manufacturing equipment 122can include sensors configured to generate and/or collect sensor dataassociated with manufacturing equipment 122. Sensor data can include avalue of one or more of temperature (e.g., heater temperature), spacing(SP), pressure, high frequency radio frequency (HFRF), voltage ofelectrostatic chuck (ESC), electrical current, flow, power, voltage,etc. Sensor data can be associated with or indicative of manufacturingparameters such as hardware parameters, such as settings or components(e.g., size, type, etc.) of the manufacturing equipment 122, or processparameters of the manufacturing equipment 122. The sensor data can beprovided while the manufacturing equipment 122 is performing a substrateprocess. The sensor data can be different for each substrate.

In some embodiments, manufacturing equipment 122 can include metrologyequipment 126. Metrology equipment 126 can be configured to generatemetrology data associated with substrates processed by manufacturingequipment 122. The metrology data can include a value of one or more offilm property data (e.g., wafer spatial film properties), dimensions(e.g., thickness, height, etc.), dielectric constant, dopantconcentration, density, defects, etc. In some embodiments, the metrologydata can further include a value of one or more surface profile propertydata (e.g., an etch rate, an etch rate uniformity, a critical dimensionof one or more features included on a surface of the substrate, acritical dimension uniformity across the surface of the substrate, anedge placement error, etc.). The metrology data can be of a finished orsemi-finished product. The metrology data can be different for eachsubstrate.

Metrology equipment 126 can be configured to generate metrology dataassociated with a substrate before or after a substrate process.Metrology equipment 126 can be integrated with a station of the processtool of manufacturing equipment 122. In some embodiments, metrologyequipment 126 can be coupled to or be a part of a station of the processtool that is maintained under a vacuum environment (e.g., a processchamber, a transfer chamber, etc.). Such metrology equipment 126 isreferred to as integrated metrology equipment 128. Accordingly, thesubstrate can be measured by the integrated metrology equipment 128while the substrate is in the vacuum environment. For example, after asubstrate process (e.g., an etch process, a deposition process, etc.) isperformed for the substrate, the metrology data for the processedsubstrate can be generated by integrated metrology equipment 128 withoutthe processed substrate being removed from the vacuum environment. Inother or similar embodiments, metrology equipment 126 can be coupled toor be a part of the process tool station that is not maintained under avacuum environment (e.g., a factory interface module, etc.). Suchmetrology equipment 126 is referred to as inline metrology equipment130. Accordingly, the substrate is measured by inline metrologyequipment 130 outside of the vacuum environment.

Additionally or alternatively to metrology equipment 126, systemarchitecture 100 can include metrology equipment 132. Metrologyequipment 132 can include metrology measurement devices that areseparate (i.e., external) from manufacturing equipment 122. For example,metrology equipment 132 can be standalone equipment that is not coupledto any station of manufacturing equipment 122. For a measurement to beobtained for a substrate using metrology equipment 132, a user of amanufacturing system (e.g., an engineer, an operator) can cause asubstrate processed at manufacturing equipment 122 to be removed frommanufacturing equipment 122 and transferred to metrology equipment 132for measurement. In some embodiments, metrology equipment 132 cantransfer metrology data generated for the substrate to the client device120 coupled to metrology equipment 132 via network 140 (e.g., forpresentation to a manufacturing user, such as an operator or anengineer). In other or similar embodiments, the manufacturing systemuser can obtain metrology data for the substrate from metrologyequipment 132 and can provide the metrology data to system architecturevia a graphical user interface (GUI) of client device 120.

Data store 150 can be a memory (e.g., random access memory), a drive(e.g., a hard drive, a flash drive), a database system, or another typeof component or device capable of storing data. Data store 150 caninclude multiple storage components (e.g., multiple drives or multipledatabases) that can span multiple computing devices (e.g., multipleserver computers). The data store 150 can store spectral data,non-spectral data (e.g., sensor data), metrology data, predictive data,and so forth. Spectral data can include historical spectral data (e.g.,spectral data generated for a previous substrate processed atmanufacturing equipment 122 or at other manufacturing equipment coupledto data store 150 via network 140) and/or current spectra (spectral datagenerated for a current substrate being processed at manufacturingequipment 122). Current spectral data can be data for which predictivedata is generated. In some embodiments, metrology data can includehistorical metrology data (e.g., metrology measurement values for aprior substrate processed at the manufacturing equipment 122 or at othermanufacturing equipment). The data store 150 can also store contextualdata associated with a substrate being processed at the manufacturingsystem (e.g., recipe name, recipe step number, preventive maintenanceindicator, operator, etc.).

One or more portions of data store 150 can be configured to store datathat is not accessible to a user of the manufacturing system. In someembodiments, all data stored at data store 150 can be inaccessible bythe manufacturing system user. In other or similar embodiments, aportion of data stored at data store 150 is inaccessible by the userwhile another portion of data stored at data store 150 is accessible tothe user. In some embodiments, inaccessible data stored at data store150 is encrypted using an encryption mechanism that is unknown to theuser (e.g., data is encrypted using a private encryption key). In otheror similar embodiments, data store 150 can include multiple data storeswhere data that is inaccessible to the user is stored in a first datastore and data that is accessible to the user is stored in a second datastore.

In some embodiments, predictive system 110 includes server machine 170and server machine 180. Server machine 170 includes a training setgenerator 172 that is capable of generating training data sets (e.g., aset of data inputs and a set of target outputs) to train, validate,and/or test a machine learning model 190 or set of machine learningmodels 190. Some operations of training set generator 172 are describedin detail below with respect to FIG. 4 . In some embodiments, thetraining set generator 172 can partition the training data into atraining set, a validating set, and a testing set.

Server machine 180 can include a training engine 182. An engine canrefer to hardware (e.g., circuitry, dedicated logic, programmable logic,microcode, processing device, etc.), software (such as instructions runon a processing device, a general purpose computer system, or adedicated machine), firmware, microcode, or a combination thereof.Training engine 182 can be capable of training a machine learning model190 or a set of machine learning models 190. The machine learning model190 can refer to the model artifact that is created by the trainingengine 182 using the training data that includes training inputs andcorresponding target outputs (correct answers for respective traininginputs). The training engine 182 can find patterns in the training datathat map the training input to the target output (the answer to bepredicted), and provide the machine learning model 190 that capturesthese patterns. The machine learning model 190 can include a linearregression model, a partial least squares regression model, a Gaussianregression model, a random forest model, a support vector machine model,a neural network, a ridge regression model, and so forth.

Training engine 182 can also be capable of validating a trained machinelearning model 190 using a corresponding set of features of a validationset from training set generator 172. In some embodiments, trainingengine 182 can assign a performance rating for each of a set of trainedmachine learning models 190. A performance rating can correspond to anaccuracy of a respective trained model, a speed of the respective model,and/or an efficiency of the respective model. Training engine 182 canselect a trained machine learning model 190 having a performance ratingthat satisfies a performance criterion to be used by predictive engine114, in accordance with embodiments described herein. Further detailsregarding training engine 182 are provided with respect to FIG. 9 .

Predictive server 112 includes a predictive engine 114 that is capableof providing spectral data for a portion of a current substrate beingprocessed at manufacturing equipment 122 as input to trained machinelearning model 190 and running trained model 190 on the input to obtainone or more outputs. In some embodiments, trained model 190 run bypredictive engine 114 is selected by training engine 182 as having aperformance rating that satisfies a performance criterion, as describedabove. As described further with respect to FIG. 8 , in someembodiments, predictive engine 114 is also capable of extracting datafrom the output of the trained machine learning model 190 and using theconfidence data to determine a condition (e.g., chamber conditionmetric) of the processing chamber of the manufacturing equipment 122.

Confidence data can include or indicate a level of confidence that achamber condition metric corresponds to one or more properties of aprocessing chamber associated with current spectral data. In oneexample, the level of confidence is a real number between 0 and 1, where0 indicates no confidence that the chamber condition metric correspondsto one or more properties of the processing chamber associated with thecurrent spectral data and 1 indicates absolute confidence that thechamber condition metrics corresponds to one or more properties of theprocessing chamber associated with the current spectral data. In someembodiments, a chamber condition prediction system can use predictivesystem 110 to provide chamber condition metrics for a processing chamberprocessing a substrate at the manufacturing system 122 instead of usingthe inline metrology equipment 130, integrated metrology equipment 130,and/or external metrology equipment 132 to determine measured metrologyvalues. The chamber condition prediction system can determine a statusof the processing chamber associated with a recovery process (e.g.,chamber seasoning) performed subsequent to a PM procedure (e.g., chambercleaning), in accordance with embodiments provided herein.

It should be noted that in some other implementations, the functions ofserver machines 170 and 180, as well as predictive server 112, can beprovided by a larger or smaller number of machines. For example, in someembodiments, server machines 170 and 180 can be integrated into a singlemachine, while in some other or similar embodiments, server machines 170and 180, as well as predictive server 112, can be integrated into asingle machine. In general, functions described in one implementation asbeing performed by server machine 170, server machine 180, and/orpredictive server 112 can also be performed on client device 120. Inaddition, the functionality attributed to a particular component can beperformed by different or multiple components operating together.Further details regarding the grouping of functions of server machines170, 180, as well as predictive server 112 are provided with respect toFIG. 3 .

In embodiments, a “user” can be represented as a single individual.However, other embodiments of the disclosure encompass a “user” being anentity controlled by a plurality of users and/or an automated source.For example, a set of individual users federated as a group ofadministrators can be considered a “user.”

FIG. 2 is a top schematic view of an example manufacturing system 200,according to aspects of the present disclosure. Manufacturing system 200can perform one or more processes on a substrate 202. Substrate 202 canbe any suitably rigid, fixed-dimension, planar article, such as, e.g., asilicon-containing disc or wafer, a patterned wafer, a glass plate, orthe like, suitable for fabricating electronic devices or circuitcomponents thereon, according to aspects of the present disclosure. Insome embodiments, manufacturing system 200 can be include or be a partof system architecture 100, in accordance with embodiments describedwith respect to FIG. 1 .

Manufacturing system 200 can include a process tool 204 and a factoryinterface 206 coupled to process tool 204. Process tool 204 can includea housing 208 having a transfer chamber 210 therein. Transfer chamber210 can include one or more processing chambers (also referred to asprocess chambers) 214, 216, 218 disposed therearound and coupledthereto. Processing chambers 214, 216, 218 can be coupled to transferchamber 210 through respective ports, such as slit valves or the like.Transfer chamber 210 can also include a transfer chamber robot 212configured to transfer substrate 202 between process chambers 214, 216,218, load lock 220, etc. Transfer chamber robot 212 can include one ormultiple arms where each arm includes one or more end effectors at theend of each arm. The end effector can be configured to handle particularobjects, such as wafers.

In some embodiments, transfer chamber 210 can also include metrologyequipment, such as integrated metrology equipment 128, described withrespect to FIG. 1 . Integrated metrology equipment 128 can be configuredto generate metrology data associated with substrate 202 before orduring a substrate process, while the substrate is maintained in avacuum environment. As illustrated in FIG. 2 , integrated metrologyequipment 128 can be disposed within transfer chamber 210. In other orsimilar embodiments, integrated metrology equipment 128 can be coupledto transfer chamber 210. As integrated metrology equipment 128 isdisposed within or coupled to transfer chamber 210, metrology dataassociated with substrate 202 can be generated without substrate 202being removed from the vacuum environment (e.g., transferred to factoryinterface 206.

Process chambers 214, 216, 218 can be adapted to carry out any number ofprocesses on substrates 202. A same or different substrate process cantake place in each processing chamber 214, 216, 218. A substrate processcan include atomic layer deposition (ALD), physical vapor deposition(PVD), chemical vapor deposition (CVD), etching, annealing, curing,pre-cleaning, metal or metal oxide removal, or the like. Other processescan be carried out on substrates therein. In some embodiments, chamberstatus equipment, such as chamber status equipment 124 described withrespect to FIG. 1 , can be coupled to or disposed within a processchamber 214, 216, 218. Chamber status equipment 124 can be configured tocollect spectral data. The spectral data may include OES data associatedwith a plasma of the processing chambers 214, 216, 218 and reflectivitydata for a profile of a surface of the substrate during a substrateprocess. A processing device coupled to chamber status equipment 124(e.g., system controller 228) can determine, based on the collectedspectral data, chamber condition metrics (e.g., indicating whether thechamber is operating under normal or abnormal conditions) of aprocessing chamber 214, 216, 218 performing an etch process. In someembodiments, the one or more components of chamber status equipment 124can include components described with respect to FIG. 5 (e.g., opticalfiber bundle, collimator assembly, etc.). In other or similarembodiments, chamber status equipment 124 can include one or moresensors disposed within or outside of process chambers 214, 216, 218 andconfigured to collect spectral data for a portion of substrate 202and/or an environment within process chamber 214, 216, 218, before,after, or during a substrate process.

A load lock 220 can also be coupled to housing 208 and transfer chamber210. Load lock 220 can be configured to interface with, and be coupledto, transfer chamber 210 on one side and factory interface 206 onanother side. Load lock 220 can have an environmentally-controlledatmosphere that can be changed from a vacuum environment (whereinsubstrates can be transferred to and from transfer chamber 210) to anatmospheric-pressure (or near atmospheric-pressure) inertgas environment(wherein substrates can be transferred to and from factory interface206), in some embodiments.

Factory interface 206 can be any suitable enclosure, such as anEquipment Front End Module (EFEM). Factory interface 206 can beconfigured to receive substrates 202 from substrate carriers 222 (e.g.,Front Opening Unified Pods (FOUPs)) docked at various load ports 224 offactory interface 206. A factory interface robot 226 (shown dotted) canbe configured to transfer substrates 202 between substrate carriers(also referred to as containers) 222 and load lock 220. In other and/orsimilar embodiments, factory interface 206 can be configured to receivereplacement parts from replacement parts storage containers 222.

In some embodiments, manufacturing system 200 can include metrologyequipment that is configured to generate metrology data associated withsubstrate 202 outside of the vacuum environment. For example, asillustrated in FIG. 2 , integrated metrology equipment 128 can becoupled to a process chamber (e.g., process chamber 214, 216, and/or218). Integrated metrology equipment 128 can be configured to generatemetrology data associated with substrate 202 prior to substrate 202being placed in the vacuum environment (e.g., transferred to load lock220) and/or after substrate 202 is removed from the vacuum environment(e.g., removed from load lock 220). It should be noted that althoughFIG. 2 depicts inline metrology equipment 130 coupled to factoryinterface 206, inline metrology equipment 130 can be coupled to any partof the process tool 204 that is outside of the vacuum environment (e.g.,coupled to load lock 220, etc.).

Manufacturing system 200 can also be connected to a client device (e.g.,client device 120 of FIG. 1 ) that is configured to provide informationregarding manufacturing system 200 to a user (e.g., an operator). Insome embodiments, the client device can provide information to a user ofmanufacturing system 200 via one or more graphical user interfaces(GUIs). For example, the client device can provide information regardingone or more chamber condition metrics (e.g. while performing a substrateprocess) of a processing chamber 214, 216, 218 via a GUI.

Manufacturing system 200 can also include or be coupled to a systemcontroller 228. System controller 228 can be and/or include a computingdevice such as a personal computer, a server computer, a programmablelogic controller (PLC), a microcontroller, and so on. System controller228 can include one or more processing devices, which can begeneral-purpose processing devices such as a microprocessor, centralprocessing unit, or the like. More particularly, the processing devicecan be a complex instruction set computing (CISC) microprocessor,reduced instruction set computing (RISC) microprocessor, very longinstruction word (VLIW) microprocessor, or a processor implementingother instruction sets or processors implementing a combination ofinstruction sets. The processing device can also be one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Systemcontroller 228 can include a data storage device (e.g., one or more diskdrives and/or solid state drives), a main memory, a static memory, anetwork interface, and/or other components. System controller 228 canexecute instructions to perform any one or more of the methodologiesand/or embodiments described herein. In some embodiments, systemcontroller 228 can execute instructions to perform one or moreoperations at manufacturing system 300 in accordance with a processrecipe. The instructions can be stored on a computer readable storagemedium, which can include the main memory, static memory, secondarystorage and/or processing device (during execution of the instructions).

In some embodiments, system controller 228 can receive data from sensorsor other components (e.g., chamber status equipment 124) included on orwithin various portions of manufacturing system 200 (e.g., processingchambers 214, 216, 218, transfer chamber 210, load lock 220, etc.). Datareceived by the system controller 228 can include spectral data and/ornon-spectral data for a portion of substrate 202. For purposes of thepresent description, system controller 228 is described as receivingdata from chamber status equipment 124 coupled to or disposed withinprocess chambers 214, 216, 218. However, system controller 228 canreceive data from any portion of manufacturing system 200 and can usedata received from the portion in accordance with embodiments describedherein. In an illustrative example, system controller 228 can receivespectral data from an chamber status equipment 124 coupled to a processchamber 214, 216, 218 before, after, or during a substrate process atthe processing chamber 214, 216, 218. Data received from chamber statusequipment 124 or other sensors at manufacturing system 200 can be storedin a data store 250. Data store 250 can be included as a componentwithin system controller 228 or can be a separate component from systemcontroller 228. In some embodiments, data store 250 can be or include aportion of data store 150, as described with respect to FIG. 1 .

FIG. 3 depicts an illustrative system architecture 300 for chambercondition prediction of a processing chamber (e.g., performing asubstrate process), according to aspects of the present disclosure. Insome embodiments, chamber condition prediction system 300 can include orbe a part of one or more components of system architecture 100 and/ormanufacturing system 200. Chamber condition prediction system 300 caninclude one or more components of manufacturing equipment 122 (e.g.,chamber status equipment 124), metrology equipment 130, server machine320, and server machine 350.

As described previously, manufacturing equipment 122 can produceproducts following a recipe or performing runs over a period of time.Manufacturing equipment 122 can include a process chamber 310 configuredto perform a substrate process for a substrate according to a substrateprocess recipe. In some embodiments, process chamber 310 can be any ofprocess chamber 214, 218, 218, described with respect to FIG. 2 .Manufacturing equipment 122 can also include chamber status equipment124, as described herein. Chamber status equipment 124 can be coupled toor disposed within process chamber 310 and can collect spectral data tobe used to detect an endpoint of a step of the substrate process recipe.In some embodiments, manufacturing equipment 122 can also includeintegrated metrology equipment 128, as described herein. Integratedmetrology equipment 128 can be configured to generate metrology dataassociated with the substrate before or after the substrate process iscompleted.

Manufacturing equipment 122 can be coupled to server machine 320. Servermachine 320 can include processing device 322 and/or data store 332. Insome embodiments, processing device 322 can be configured to execute oneor more instructions to perform operations at manufacturing equipment122. For example, processing device 322 can include or be a part ofsystem controller 228, described with respect to FIG. 2 . Data store 332can include or be part of data store 150 and/or data store 250, in someembodiments.

Processing device 322 can be configured to receive data from one or morecomponents of manufacturing equipment 122 (i.e., via a network). Forexample, processing device 322 can receive spectral data 336 (e.g., OESdata of a plasma disposed within the chamber, optical reflectancemeasurement corresponding to a reflectance pattern of light reflectedoff a surface of the a substrate disposed within the processing chamber)collected by chamber status equipment 124 during a substrate process fora substrate at process chamber 310. In another example, processingdevice 322 can receive metrology data 338 collected by integratedmetrology equipment 128 before and/or after the substrate process forthe substrate. Metrology data 338 can include a metrology measurementvalue generated for the substrate by integrated metrology equipment 128.In some embodiments, processing device 322 can store the receivedspectral data and/or the received metrology data 338 at data store 332.

In some embodiments, processing device 322 can receive metrology data338 from other metrology equipment. For example, in some embodiments,server 320 can be coupled to inline metrology equipment 130 (i.e., via anetwork). A substrate can be removed from process chamber 210 andtransferred to inline metrology equipment 130, as described herein.Inline metrology equipment 130 can generate metrology data 338 for thesubstrate and transmit the generated metrology data 338 to processingdevice 322 via the network. In another example, the substrate can beremoved from manufacturing equipment 122 and can be transferred toexternal metrology equipment, such as external metrology equipment 132described with respect to FIG. 1 . A client device, such as clientdevice 120, can be coupled to server 320 (i.e., via the network). Insome embodiments, a user of manufacturing equipment 122 can obtainmetrology data 338 using external metrology equipment 132 and canprovide the obtained metrology data 338 via a GUI of client device 120.Client device 120 can transmit metrology data 338 via the network. Inadditional or alternative embodiments, external metrology equipment 132can be coupled to server 320 via the network and external metrologyequipment 132 can transmit metrology data 338 directly to processingdevice 352.

Processing device 352 can include a predictive engine 328 and a chambercondition engine 330. Predictive engine 328 can be configured to providea value for a metrology measurement based on spectral data 336 collectedfor a substrate during a substrate process. For example, predictiveengine 328 can provide spectral data 336 collected for a current processperformed for a current substrate at process chamber 310 as input to atrained machine learning model 334. Predictive engine 328 can obtain, asan output of machine learning model 334, predicted metrology data 338including an indication of the metrology measurement value thatcorresponds to the current substrate. In some embodiments, predictiveengine 328 can correspond to predictive engine 114, described withrespect to FIG. 1 .

Chamber metric engine 330 at processing device 322 can be configured todetermine chamber conditions of a processing chamber 310 performing asubstrate process. Chamber condition engine 330 can obtain or determineone or more chamber condition metrics for a process chamber 310processing a current substrate from predictive engine 328. Chambercondition metrics may include a selection of values each associated witha combination, feature, or pattern identified in the input data to thepredictive engine 328. For example, a first value may be indicative of acertain spectral data and sensor data combination at a given time. Inanother example, another value may be associated with a gradient of oneor more variable combination determine and/or identified by thepredictive engine 328. In some embodiments, the chamber conditionsmetric may include a series of values (e.g., vector, matrix, etc.)indicating a correlation of a particular data combination, correlation,pattern, and/or relationship present in the sensor data. For example,the chamber condition metric may include a feature vector includingbinary values indicating the presence or absence of a particular featurein the data.

The chamber condition metrics may be compared against a known patternand/or combination of chamber metrics (e.g., target chamber metrics).Target chamber metrics may be associated with one or more stages of arecovery process (e.g., seasoning procedure) In response to determiningthat the chamber condition metrics satisfy one or more chamber conditionthreshold (e.g., conditions relating to a recovery process of theprocess chamber), chamber condition engine 330 can modify a performanceof the processing chamber 310 (e.g., by generating an instructionincluding a command to alter a process parameter). A chamber conditionthreshold may be a specified combination of values indicated by thechamber condition metrics. For example, chamber condition engine 330 maydetermine an update to a least process recipe associated with thereceived sensor data based on determining one or more chamber conditionsare present in the process chamber based on identified patterns,features, correlations, relationship between the chamber conditionmetrics received by predictive engine 328. For example, the modificationmay be associated with a current substrate process such as processing acurrent substrate according to updated process parameters. In anotherexample, the modification may include halting a current substrateprocess. In another example, the modification may include a change to aprocess recipe used to process a future substrate. Processing device 322can transmit the instruction to manufacturing equipment 122, causing themodification to the identified performance of the processing chamber.For example, the instruction may command the process chamber to continueto process a substrate according to an updated set of processparameters. In another example, the instruction may include causingsubstrate processing within the processing chamber to halt. In Someembodiments, the target chamber metrics is associated with a stage of aPM recovery procedure. Further details associated with predictingchamber conditions of a processing chamber processing a substrateaccording to a current process are provided with respect to FIG. 7 .

As illustrated in FIG. 3 , processing device 322 can include a trainingset generator 324 and/or a training engine 326, in some embodiments. Insome embodiments, training set generator 324 can correspond to trainingset generator 172 and/or training engine 326 can correspond to trainingengine 182, described with respect to FIG. 1 . Training set generator324 can be configured to generate training sets 340 to train machinelearning model 334 or a set of machine learning models 334. For example,training set generator 324 can generate a training input based onhistorical spectral data 336 associated with a prior substrate. In someembodiments, training set generator 324 can retrieve the historicalspectral data 336 from data store 332 to generate the training input.Training set generator 324 can generate a target output indicating achamber condition (e.g., chamber condition metrics) for the traininginput based on historical metrology data 338 obtained for the priorsubstrate. As described above, historical metrology data 338 can begenerated by inline metrology equipment 130, integrated metrologyequipment 128, or external metrology equipment 132. Training setgenerator 324 can include the generated training input and the generatedtarget output in a training set 340. Further details regardinggenerating training set 340 are provided with respect to FIG. 4 .

Training engine 326 can be configured to train, validate and/or test themachine learning model 334 or sets of machine learning models 334.Training engine 326 can provide training set 340 to train machinelearning model(s) 334 and store trained machine learning model(s) 334 atdata store 332. In some embodiments, training engine 326 can use avalidation set 342 to validate a trained machine learning model 334.Validation set 342 can include spectral data 336 and chamber conditionmetrics corresponding to metrology data 338 obtained for a priorsubstrate (i.e., processed at process chamber 310 or at another processchamber). Training set generator 324 and/or training engine 326 cangenerate validation set 342 based on historical spectral data 336 andhistorical metrology data 338 obtained for a prior substrate. In someembodiments, validation set 342 can include historical spectral data 336and historical metrology data 338 that is different from historicalspectral data 336 and historical metrology data 338 included in trainingset 340.

Training engine 326 can provide the spectral data 336 (and sensor datain some embodiments) for the prior substrate as an input to a trainedmachine learning model 334 and can extract a chamber condition metricfor a processing chamber processing the prior substrate from one or moreoutputs of the trained model 334. Training engine 326 can assign aperformance score to the trained model 334 based on an accuracy of thechamber condition metric for the processing chamber processing the priorsubstrate in view a measured chamber condition of the metrology data 338for the prior substrate included in the validation set 342. Trainingengine 326 can select the trained model 334 to be used to provide futuremetrology measurement values for future substrates processed at theprocess chamber 310 in response to determining the performance scoresatisfies a performance score criterion (e.g., exceeds a performancescore threshold). Further details regarding selecting a trained model334 are provided with respect to FIG. 9 .

As discussed previously, training set generator 324 and/or trainingengine 326 can be components of processing device 322 at server 320, insome embodiments. In additional or alternative embodiments, training setgenerator 324 and/or training engine 326 can be components of processingdevice 352 at server 350. Server 350 can include or be part of acomputing system that is separate from manufacturing system 200. Asdescribed previously, server 320 can include or be part of systemcontroller 228, described with respect to FIG. 2 , in some embodiments.In such embodiments, server 350 can include or be part of a computingsystem that is coupled to system controller 228 (i.e., via a network),but is separate from system controller 228. For instance, a user ofmanufacturing system 200 may be provided with access to data stored atone or more portions of data store 332 or one or more processes executedat processing device 322. However, the user of manufacturing system 200may not be provided with access to any data stored at one or moreportions of data store 354 or any processes executed at processingdevice 352.

Processing device 352 can be configured to execute training setgenerator 324 and/or training engine 326 in a similar fashion asprocessing device 322. In some embodiments, server 350 can be coupled tomanufacturing equipment 122 and/or inline metrology equipment 130 via anetwork. As such, processing device 352 can obtain spectral data 336 andchamber condition metrics corresponding to metrology data 338 to be usedby training set generator 324 and/or training engine 326 to generatetraining set 340 and validation set 342, in accordance with embodimentsdescribed with respect to processing device 322. In other or similarembodiments, server 350 is not coupled to manufacturing equipment 122and/or external metrology equipment 132. Accordingly, processing device352 can obtain spectral data 336 and/or metrology data 338 fromprocessing device 322. For example, processing device 322 can receivespectral data 336 from chamber status equipment 124, as previouslydescribed. Processing device 322 can transmit the received spectral data336 to processing device 352 (i.e., via a network). Processing device352 can store spectral data 336 at data store 354, in some embodiments.In some embodiments, processing device 322 can similarly transmitmetrology data 338 obtained for a substrate to processing device 352.For example, processing device 322 can receive metrology data 338 frominline metrology equipment 130, integrated metrology equipment 128and/or external metrology equipment 132, as previously described.Processing device 352 can transmit the received metrology data 338 fromprocessing device 322 and, in some embodiments, store metrology data 338at data store 354.

Training set generator 324 at processing device 352 can generatetraining set 340 in accordance with previously described embodiments.Training engine 326 at processing device 352 can train and/or validatemachine learning model 334, in accordance with previously describedembodiments. In some embodiments, server 350 can be coupled to othermanufacturing equipment and/or other server machines that are differentfrom manufacturing equipment 122 and/or server machine 320. Processingdevice 352 can obtain spectral data 336 and/or metrology data 338 fromthe other manufacturing equipment and/or server machines, in accordancewith embodiments described herein. In some embodiments, training set 340and/or validation set 342 can be generated based on spectral data 336and metrology data 338 obtained for substrates processed at processchamber 310 as well as other spectral data and metrology data obtainedfor other substrates processed at process chambers at othermanufacturing systems.

In response to training engine 326 selecting trained model 334 to beapplied to future spectral data for future substrates at process chamber310, processing device 352 can transmit trained model 334 to processingdevice 322. Predictive engine 328 can use trained model 334 to providemetrology measurement values for future substrates at process chamber310, as previously described.

FIG. 4 is a flow chart of a method 400 for training a machine learningmodel, according to aspects of the present disclosure. Method 400 isperformed by processing logic that can include hardware (circuitry,dedicated logic, etc.), software (such as is run on a general purposecomputer system or a dedicated machine), firmware, or some combinationthereof. In one implementation, method 400 can be performed by acomputer system, such as system architecture 100 of FIG. 1 . In other orsimilar implementations, one or more operations of method 400 can beperformed by one or more other machines not depicted in the figures. Insome aspects, one or more operations of method 400 can be performed bytraining set generator 324 of server machine 320 or server machine 350,described with respect to FIG. 3 .

At block 410, processing logic initializes a training set T to an emptyset (e.g., {}). At block 412, processing logic obtains spectral dataand/or chamber data associated with a substrate processed at a processchamber of a manufacturing system. In some embodiments, the spectraldata and/or chamber data can be received from one or more sensorsdisposed within or coupled to the process chamber. In other or similarembodiments, the spectral data can be received from chamber statusequipment disposed within or coupled to the process chamber.

Referring back to FIG. 4 , at block 414, processing logic obtainsmetrology data for the substrate. As described previously, metrologydata can include a value of one or more of film property data (e.g.,wafer spatial film properties), dimensions (e.g., thickness, height,etc.), dielectric constant, dopant concentration, density, defects, etc.In some embodiments, the metrology data can further include a value ofone or more surface profile property data (e.g., an etch rate, an etchrate uniformity, a critical dimension of one or more features includedon a surface of the substrate, a critical dimension uniformity acrossthe surface of the substrate, an edge placement error, etc.). In someembodiments, the metrology measurements can be received from metrologyequipment of manufacturing system 200 (e.g., inline metrology equipment130, integrated metrology equipment 128, etc.). For example, after thesubstrate process is completed, the substrate can be transferred toinline metrology equipment 130 or integrated metrology equipment 130 ofmanufacturing system 200. Inline metrology equipment 130 or integratedmetrology equipment 128 can generate metrology data associated with thesubstrate and can transmit the metrology data to a computing deviceincluding the processing logic (e.g., server machine 320, server machine350, etc.) via a network. In other or similar embodiments, the metrologymeasurements can be received from metrology equipment that is separatefrom manufacturing system 200 (e.g., external metrology equipment 132),as described herein.

At block 416, processing logic generates a training input based on thespectral data obtained for the substrate at block 412. In someembodiments, the training input can include a normalized set of spectraldata (e.g., including both OES data and substrate surface reflectometerdata as described herein) generated based on the spectral data obtainedfor the processing device and the substrate. The normalized set ofspectral data can include one or more spectral features that correspondto a particular type of metrology measurement. The spectral features maybe based on combinations of optimal emission measurements and opticalreflectance spectra measurements. Further details regarding generatingthe training input are described with respect to FIG. 6 . At block 418,processing logic can generate a target output based on the metrologydata obtained for the substrate at block 414. The target output cancorrespond to chamber condition metrics (data indicative of patterns ofmetrology measurements) corresponding to metrology measurementsassociated with the substrate. For example, at block 414, processinglogic can obtain metrology data indicating a thickness of a film at oneor more portions of a surface for a substrate after an etch process. Thethickness may be indicate of a condition of the chamber (e.g., a chamberthat is not fully recovered from a recovery process or cold chamber).Processing logic can generate a target output corresponding theidentified chamber condition (e.g., abnormal chamber condition, normalchamber conditions, not fully recovered, not fully seasoned, etc.).

At block 420, processing logic generates an input/output mapping. Theinput/output mapping refers to the training input that includes or isbased on data for the substrate, and the target output for the traininginput, where the target output identifies a metrology measurement valuefor the substrate, and where the training input is associated with (ormapped to) the target output. At block 422, processing logic adds theinput/output mapping to the training set T.

At block 424, processing logic determines whether the training set, T,includes a sufficient amount of training data to train a machinelearning model. It should be noted that in some implementations, thesufficiency of training set T can be determined based simply on thenumber of input/output mappings in the training set, while in some otherimplementations, the sufficiency of training set T can be determinedbased on one or more other criteria (e.g., a measure of diversity of thetraining examples, etc.) in addition to, or instead of, the number ofinput/output mappings. Responsive to determining the training set, T,includes a sufficient amount of training data to train the machinelearning model, processing logic provides the training set, T, to trainthe machine learning model. Responsive to determining the training setdoes not include a sufficient amount of training data to train themachine learning model, method 400 returns to block 412.

At block 426, processing logic provides the training set T to train themachine learning model. In some embodiments, the training set T isprovided to training engine 326 of server machine 320 and/or servermachine 350 to perform the training. In the case of a neural network,for example, input values of a given input/output mapping (e.g.,spectral data and/or chamber data for a previous substrate) are input tothe neural network, and output values of the input/output mapping arestored in the output nodes of the neural network. The connection weightsin the neural network are then adjusted in accordance with a learningalgorithm (e.g., backpropagation, etc.), and the procedure is repeatedfor the other input/output mappings in the training set T. After block426, machine learning model 190 can be used to provide chamberconditions (e.g., chamber condition metrics) for future processingchambers processing future substrates (for example, in accordance withmethod 800 of FIG. 8 described below).

FIG. 5 is a cross-sectional schematic side view of chamber statusequipment 124 coupled to a process chamber 310, according to aspects ofthe present disclosure. In some embodiments, process chamber 310 can beused for processes in which a corrosive plasma environment is provided.For example, the process chamber 310 can be a chamber for a plasmaetcher or plasma etch reactor, a plasma cleaner, and so forth. In otheror similar embodiments, process chamber 310 can be used for processes inwhich a non-corrosive environment is provided. For example, processchamber 310 can be used as a chemical vapor deposition (CVD) chamber, aphysical vapor deposition (PVD) chamber, an atomic layer deposition(ALD) chamber, an ion assisted deposition (IAD) chamber, and other typesof processing chambers.

Briefly, process chamber 310 includes a chamber body 502 and a lid 530and/or a showerhead (not shown) that encloses an interior volume 506.Chamber body 502 generally includes sidewalls 508 and a bottom 510. Theshowerhead can include a showerhead base and a showerhead gasdistribution plate. The lid 530 and/or the showerhead can be supportedon sidewall 508 of the chamber body 502. The lid 530 (or showerhead) canbe opened to allow access to the interior volume 506 of process chamber310, and can provide a seal for the process chamber 310 while closed. Agas panel (not shown) can be coupled to process chamber 310 to provideprocess and/or cleaning gases to interior volume 506 through lid 530 anda nozzle (e.g., through apertures of the showerhead or lid and nozzle)and/or the showerhead. An exhaust port 526 can be defined in chamberbody 502, and can couple interior volume 506 to a pump system 528. Pumpsystem 528 can include one or more pumps and throttle valves utilized toevacuate and regulate the pressure of interior volume 506 of processchamber 310. A substrate support assembly 548 is disposed in interiorvolume 506 lid 530 and/or the showerhead. Substrate support assembly 548holds a substrate, such as substrate 202 of FIG. 2 , during processing.In one embodiment, substrate support assembly 548 includes a pedestal552 that supports an electrostatic chuck 550.

Chamber status equipment 124 can be configured to optically monitor anenvironment of interior volume 506 during a substrate process forsubstrate 202. In some embodiments, chamber status equipment 124 can bemechanically coupled to chamber body 502 and optically interfaced (i.e.,via optical interface 570) with the environment of interior volume 506.Chamber status equipment 124 can include a collimator assembly 554, anoptical fiber bundle 556, a light component 562, a processing device 576and, in some embodiments, a polarizer component 586. As illustrated inFIG. 5 , collimator assembly 554 can be coupled to chamber interface570. In some embodiments, chamber interface 570 can be an orifice, aconverging or diverging lens, a transparent slab, or any other device ormaterial that is capable of transferring light between collimatorassembly 554 and the environment of interior volume 506. It should benoted that although FIG. 5 depicts chamber interface 570 as beingembedded within lid 530, chamber interface 570 can be embedded within orcoupled to any portion of process chamber 310 (e.g., sidewall 508,bottom 510, etc.).

A first end of optical fiber bundle 556 can be coupled to collimatorassembly 554 and a second end of optical fiber bundle 556 can be coupledto light component 562. Light component 562 can include a light source564 configured to generate light. Herein, “light” refers toelectromagnetic radiation of any spectral range, including visible, farand near infrared (IR), far and near ultraviolet (UV), and so forth.“Light” can further include unpolarized (e.g., natural) light, linearly,circularly, or elliptically polarized light, partially-polarized light,focused light, diverging light, collimated light, and so on. In someembodiments, light source 564 can include a narrow-band light source,such as a light-emitting diode (LED), a laser, a light bulb, etc. Inother or similar embodiments, light source 564 can include a broadbandlight source. Light source 564 can include more than one component lightsources, such as multiple narrow-band light sources producing (whentaken together) a broadband light input, in some embodiments. Lightsource 564 can include additional optical elements (i.e., filters,absorbers, polarizers, etc.) to control a spectral distribution and/orpolarization of the light.

Light generated by light source 564 (referred to as input light herein)can be transmitted to collimator assembly 554 through one or moreemitting optical fibers 558 of optical bundle 556. In response toreceiving the input light via emitting optical fibers 558, collimatorassembly 554 can be configured to convert the input light into a lightbeam 572. For example, the input light can pass via one or more opticalelements of collimator assembly 554, such as lenses, reflectors,filters, apertures, and so forth. In some embodiments, spatialproperties of the light beam produced by collimator assembly 554 can bethe same for multiple spectral components of light beam 572. Forexample, a diameter of light beam 572 can be the same within a broadrange of wavelengths λ of various spectral components contained in theinput light and, therefore in light beam 572. In some embodiments,collimator assembly 554 can include one or more achromatic lenses.Accordingly, the light beam 572 generated by collimator assembly 554 canbe an achromatic light beam.

As illustrated in FIG. 5 , in some embodiments, collimator assembly 554can include a polarizer component 568. Polarizer component 568 isconfigured to polarize unpolarized (e.g., natural) light generated bylight source 564. For example, polarizer component 568 can convertunpolarized input light into linearly, circularly, or ellipticallypolarized light. It should be noted that although FIG. 5 illustratespolarizer component 568 as being part of collimator assembly 554,polarizer component 568 can be coupled to any portion of chamber statusequipment 124 that passes input light to optical interface 570. Forexample, polarizer component 568 can be coupled to an outlet of lightsource 564, to an outlet of the one or more emitting optical fibers 558,between collimator assembly and optical interface 570, etc.

Collimator assembly 554 can direct light beam 572 to a surface ofsubstrate 202 disposed on substrate support assembly 548 via opticalinterface 570. Light beam 572 can be reflected off the surface ofsubstrate 202 as reflected light beam 574, which is received bycollimator assembly 554. One or more receiving optical fibers 560 ofoptical bundle 556 can transmit reflected light beam 574 to lightdetector 566 of light component 562. Light detector 566 can include oneor more spectrographs, spectrometers, diffraction gratings, mirrors,lenses, photodiodes, and other devices. Light detector 566, alone or inconjunction with processing device 576, can determine one or moreoptical responses associated with the surface of substrate 202 based onreflected light beam 574. For example, light detector 566 and/orprocessing device 576 can determine a reflectivity R(λ), a refractionindex n(λ), or any other optical quantity that can be used tocharacterize substrate 202 based on reflected light 574. In someembodiments, the optical responses can be used to characterize, forsubstrate 202, a polarization dependence of the reflectivity, an angleof rotation of the polarization plane upon reflection, luminescenceintensity, and so on. Spectral data, as described with respect to thisapplication, can refer to data corresponding to the optical responses ofreflected light 574 and/or the optical characteristics for substrate 202derived from the optical responses of reflected light 574.

Chamber status equipment 124 may include an optic sensor 582 thatcaptures plasma emissions of a plasma disposed within interior volume506. The optic sensor 582 is coupled to a fiber optic 584 that carriesan optical signal corresponding to the plasma emissions to lightdetector 566. Light detector 566 may include an optical emissionspectrometer (OES). The OES analyzes the optical signal received fromfiber optic 584 to identify emission peaks and patterns within thesignal, including identifying specific emission peaks as correspondingto energy transition of specific elements. In some embodiments, spectraand/or information characterizing emission peaks therein may be viewedand/or manipulated on OES. In some of these and in other embodiments,the spectral data can include emission peak information. The spectraldata may be transferred to processing device 576 for further processing.

Chamber status equipment 124 may include one or more sensors 512configured to generate and/or collect sensor data associated with theprocessing chamber 310. Sensor data can include a value of one or moreof temperature (e.g., heater temperature), spacing (SP), pressure, highfrequency radio frequency (HFRF), voltage of electrostatic chuck (ESC),electrical current, flow, power, voltage, etc. Sensor data can beassociated with or indicative of manufacturing parameters such ashardware parameters, such as settings or components (e.g., size, type,etc.) of the manufacturing equipment, or process parameters of themanufacturing equipment. The sensor data can be provided while theprocessing device 310 is performing a substrate process. The sensor datacan be different for each substrate. The sensor data may be provided toprocessing device 576.

In some embodiments, processing device 576 can be included as part of asystem controller (e.g., system controller 228) for a manufacturingsystem including process chamber 310. In such embodiments, processingdevice 576 can store the spectral data generated for substrate 202 atdata store coupled to processing device 576 (e.g., data stores 250, 332,354, etc.). In other or similar embodiments, processing device 576 canbe a processing component that is separate from the system controllerbut is coupled to the system controller via a network. Processing device576 can transmit the generated spectral data to the system controllerfor storage at a respective data store of the manufacturing system.

FIG. 6 is a flow chart of a method 600 for training and/or updating amachine learning model for predicting a chamber condition of aprocessing chamber, according to aspects of the present disclosure.Method 600 is performed by processing logic that can include hardware(circuitry, dedicated logic, etc.), software (such as is run on ageneral purpose computer system or a dedicated machine), firmware, orsome combination thereof. In one implementation, method 600 can beperformed by a computer system, such as system architecture 100 of FIG.1 . In other or similar implementations, one or more operations ofmethod 600 can be performed by one or more other machines not depictedin the figures. In some aspects, one or more operations of method 600can be performed by chamber condition engine 330 of server machine 320,described with respect to FIG. 3 .

At block 602, processing logic perform data collection. Data collectionmay include receiving sensor data including chamber data and spectraldata (e.g., OES and reflectometry data), metrology, data labels (processyield, electrical measurements, etc.) from a manufacturing system. Thedata may be associated with one or more processing chambers processing aone or more substrates. The sensor data may include data acquired bychamber status equipment 124, integrated metrology equipment 128, and/orinline metrology equipment 130. In some embodiments, the spectral datacan include optical emission spectra (OES) measurements of a plasmadisposed within the processing chamber. In some embodiments, thespectral data can include optical reflectance spectra measurementscorresponding to a reflectance pattern of light reflected off a surfaceof the substrate disposed within the processing chamber. In someembodiments the spectra and chamber data can be received from chamberstatus equipment, such as chamber status equipment 124, or other sensorsdisposed within or coupled to the process chamber, in accordance withpreviously described embodiments.

At block 604, processing logic performs data pre-processing includingdetermining one or more algorithms, size, and speed thresholdrequirements of a machine-learning to be trained using sensor data.Processing logic generates normalized sensor data that includes anindication of a spectral feature. Processing logic may perform functionsand/or methodology to extract features and/or generatesynthetic/engineered data associated with the received sensor data. Forexample, processing logic may perform a feature extraction of thespectral data to identify spectral features. Spectral features maycorrespond to identified correlations, pattern, and/or abnormalities ofthe spectral data. In another example, processing logic may perform afeature extraction using combinations of spectral data to determinewhether a criterion is satisfied. Processing logic can analyze multipledata points of an associated parameter to determine whether rapidchanges occurred to the parameter during a portion of a substrateprocess. In some embodiments, processing logic performs a normalizationacross the various sensor data (e.g., spectral data and/or chamber data)associated with various process chamber conditions. A normalization mayinclude processing the incoming sensor data (e.g., spectral data and/orchamber data) to appear similar across the various chambers and sensorsused to acquire the data.

In some embodiments, processing logic performs one or more of a dataextrapolation procedure and/or a data interpolation procedure to obtainadditional data beyond the received measured data points. Processinglogic may predict spectral data at time different that the measuredvalues. For example, processing logic may identify one or more features,patterns, and/or relationship between individual data points of thespectral data and use an interpolation procedure to determineestimations of spectral data points (e.g., at different time intervalsor at time when a measurement did not occur) occurring at points betweencaptured data points of the measured spectral data (e.g., OESmeasurements). In another example, processing logic may identify one ormore features, patterns, and/or relationships between individual datapoints of the spectral data and use an extrapolation procedure todetermine spectral data points occurring outside the window of time(e.g., spectral data predictions associated with instances before and/orafter the acquisition of the spectral data) corresponding to themeasured data points of the spectral data.

Processing logic may split the received sensor data into a training setand/or a validation set. The processing logic may further sort the datainto groups and assign individual data combinations to one or both ofthe training set and/or validation set.

At block 606, processing logic performs model training on a set of oneor more machine learning algorithms. Each of the set of machine learningmodels can correspond to a different machine learning model type. Forexample, each of the set of machine learning models can correspond to alinear regression model, a partial least squares regression model, aGaussian regression model, a random forest model, a support vectormachine model, a neural network, a ridge regression model, a logisticregression type algorithm, a multi-layer perception algorithm, arecurrent neural network (RNN), and so forth. Performing the training ofthe set of machine learning model may be performed in accordance withembodiments described with respect to method 400 of FIG. 4 .

In some embodiments the trained machine learning model is to receive anew input having new sensor data having new chamber data indicating anew state of a new environment of a new processing chamber processing anew substrate according to a new process and new spectral dataindicating optical emission spectra (OES) measurements of a new plasmadisposed within the new processing chamber processing the new substrateaccording to the new process to produce a new output based on the newinput. The new output indicates a chamber condition metric correspondingto a recovery status associated with a chamber recovery processperformed subsequent to a preventative maintenance procedure.

At block 608, processing logic performs validation of the one or moretrained machine learning models. Results and statistics from both thetraining data set and the validation data set are compared and a “best”overall model is selected. Model selection may be determined accordingto method 900 of FIG. 9 . Validation may include determining an accuracyof each of the trained machine learning models based on a correspondingset of features of each training set. Processing logic may discardtrained machine learning models that have an accuracy that does not meeta threshold accuracy. Processing logic may determine a trained machinelearning model that has the highest accuracy of all of the trainedmachine learning models based on the testing (and, optionally,validation) sets.

At block 610, one or more models are installed on a server (e.g., anon-tool server). The one or more selected models receive sensor data(e.g., spectral data and chamber data) of a new substrate and predictone or more chamber condition metrics corresponding to process resultpredictions in one embodiment. A process result prediction may encompassone or more values indicating an estimation of a process result (e.g.,film thickness, critical dimension, side wall angle, etc.) of asubstrate corresponding to the received sensor data. The chambercondition metrics may include a selection of values each associated witha combination, feature, or pattern identified in the input data to thepredictive engine 328. For example, a first value may be indicative of acertain spectral data and sensor data combination at a given time. Inanother example, another value may be associated with a gradient of oneor more variable combination determine and/or identified by thepredictive engine 328. In some embodiments, the chamber conditionsmetric may include a series of values (e.g., vector, matrix, etc.)indicating a correlation of a particular data combination, correlation,pattern, and/or relationship present in the sensor data. For example,the chamber condition metric may include a feature vector includingbinary values indicating the presence or absence of a particular featurein the data. The chamber condition metrics may be used to alter aperformance of the processing chamber. For example, the chamberconditions may be leveraged to update a process parameter of a processrecipe. The updates may be based on a difference of determined chambercondition metrics and a target chamber condition metrics associated witha process recipe. In another example, the processing logic may cause anotification to be displayed on a graphical user interface (GUI). Thenotification may indicate a modification to be taken by the processchamber (or more generally, the manufacturing equipment). For example,processing logic may cause a currently processed substrate or a newsubstrate to be processed according to an updated set of processparameters. In another example, processing logic may cause a substrateprocessed within the processing chamber to halt.

At block 612, processing logic perform in-situ data collection of a livesubstrate process environment. The collected data may include spectraldata, metrology data, data labels (e.g., process results such as yield,metrology data, electrical data, etc.). The data may be associated withone or more processing chambers currently processing a substrate. Thesensor data may include data acquired by chamber status equipment 124,integrated metrology equipment 128, and/or inline metrology equipment130. In some embodiments, the spectral data can include optical emissionspectra (OES) measurements of a plasma disposed within the processingchamber. In some embodiments, the spectral data can include opticalreflectance spectra measurements corresponding to a reflectance patternof light reflected off a surface of the substrate disposed within theprocessing chamber. In some embodiments the spectra and chamber data canbe received from chamber status equipment, such as chamber statusequipment 124, or other sensors disposed within or coupled to theprocess chamber, in accordance with previously described embodiments.

At block 614, processing logic determines if there is an anomaly in thereceived in-situ data collected in association with a substrate processbased on a comparison of the received data and historical sensor data.Processing logic determines whether metrology and process result labelsare satisfactory (e.g., meeting a threshold performance rating) based ona comparison between the metrology data and historical metrology dataand associated labels. Processing logic may determine the absence of aprocessing anomaly and proceeds along the no path to block 610, andcontinue to process further sensor data and process result dataassociated the current processing chamber and substrate or with one ormore additional processing chamber and/or one or more additionalsubstrates. Processing logic may determine the presence of an anomalywithin the received data sets, and proceed along the yes branch to block616.

In some embodiments, processing logic inputs the received data (e.g.,sensor data, spectral data, chamber data, process result data) into amodel (e.g., a statistical model). Processing logic receives one or moreoutputs from the statistical model. The one or more outputs may indicatea level of confidence that temporally associated data points of thechamber data, spectral data, and/or process result data accuratelyindicate conditions (e.g., cold chamber, recovered chamber,malfunctioning chamber) of the processing chamber. The statistical modelmay be generated using a regression between historical chamber data,historical spectral data, and/or historical process result data.Processing logic may determine that the level of confidence meets athreshold to determine the presence or absence of an anomaly within thereceived data.

In some embodiments the statistical model is generated using statisticalprocess control (SPC) analysis to determine control limits for thereceived data and identify data as being more or less dependable basedon those control limits. In some embodiments, the statistical model isassociated with univariate and/or multivariate data analysis (e.g., ofthe historical data). For example, various parameters can be analyzedusing the statistical model to determine patterns and correlationsthrough statistical processes (e.g., range, minimum, maximum, quartiles,variance, standard deviation, and so on). In another example,relationships between multiple variables (e.g., chamber data andspectral data) can be ascertained using regression analysis, pathanalysis, factor analysis, multivariate statistical process control(MCSPC) and/or multivariate analysis of variance (MANOVA).

At block 616, chamber examination is performed. Chamber examination mayinclude an inspection of individual components of a manufacturingsystem. Processing logic may cause a notification to be displayed on aGUI indicating an error, a data anomaly, and/or a corrective action tobe taken. In some embodiments, processing logic initiates the creation,training, validation, and/or selection of one or more new models toperform the actions described herein. In some embodiments processinglogic may update the currently used model to correct for the detectedanomalies. For example a user may load an old model that specifies thetype of mode, the settings/parameters, and old data used to create themodel. Processing logic may use the old model as a starting place (e.g.,initial guess) for training an updated model. Additional data may beadded to the system and used to further train and/or validate the modelto generate an updated model.

FIG. 7 is a flow chart of a method 700 for training and/or updating amachine learning model for predicting chamber conditions of a processingchamber, according to aspects of the present disclosure. Method 700 isperformed by processing logic that can include hardware (circuitry,dedicated logic, etc.), software (such as is run on a general purposecomputer system or a dedicated machine), firmware, or some combinationthereof. In one implementation, method 700 can be performed by acomputer system, such as system architecture 100 of FIG. 1 . In other orsimilar implementations, one or more operations of method 700 can beperformed by one or more other machines not depicted in the figures. Insome aspects, one or more operations of method 700 can be performed bychamber condition engine 330 of server machine 320, described withrespect to FIG. 3 .

At block 710, processing logic receives sensor data (e.g., spectraldata, chamber data, etc.) associated with a current substrate (e.g.,real-time in-situ data) processed according to a current process in aprocessing chamber at manufacturing system 200. In some embodiments,processing logic can receive the current spectral data from chamberstatus equipment 124 or other sensors disposed within or coupled to aprocess chamber performing the current process, as previously described.In some embodiments, processing logic can receive the current sensordata at particular time periods of the current process. For example,chamber status equipment 124 can be configured to collect sensor datafor the current substrate at particular intervals (e.g., once everysecond) during the substrate process. Chamber status equipment 124 cancollect the sensor data at a respective interval of the substrateprocess and transmit the spectral data to server machine 320, where itis received by processing logic of chamber condition engine 330.

At block 712, processing logic generates normalized sensor data (e.g.,data pre-process) that includes an indication of a spectral feature.Processing logic may perform functions and/or methodology to extractfeature and/or generate synthetic/engineered data associated with thereceived sensor data. For example, processing logic may perform afeature extraction of the spectral data to identify spectral features.In another example, processing logic may perform a feature extractionusing combinations of spectral data to determine whether a criterion issatisfied. In some embodiments, processing logic can perform principalcomponent analysis (PCA) to select the most important features of thespectral data. A principal component analysis refers to an analysis of acollection of points in a real coordinate space to perform a change ofbasis on the collection of points. In some embodiments, the set ofspectral features includes a range of wavelengths of the detected lightthat correspond with the individual chamber condition metrics. In suchembodiments, processing logic can identify the particular wavelengthsbased on analyzing the spectral data. For example, processing logic canprovide data associated with a structure of the substrate (e.g., CD,thickness, material property, SWA, etc.) as input to a wave analysismodel and extract one or more outputs. Processing logic can determine,based on the one or more outputs that a particular range of the lightspectrum correspond to the particular type of metrology measurement andthat wavelengths X and Y are included in the particular range. Inadditional or alternative embodiments, the outputs of the wave analysismodel can indicate that wavelengths X and Y correspond to the particulartype of metrology measurement. In response to identifying the particularwavelengths that correspond to the particular type of metrologymeasurement, processing logic can extract a set of spectral data thatcorresponds to wavelengths X and Y from the normalized spectral data.

In some embodiments, the set of spectral features includes spectraltrends or patterns that are present in spectral data for one or morewavelengths that correspond with the particular type of metrologymeasurement corresponding to particular chamber conditions. Processinglogic can perform one or more analysis operations (e.g., rigorouscoupled wave-analysis (RCWA)) for normalized spectral data for the oneor more wavelengths to identify spectral trends or patterns thatcorrespond with the particular type of metrology measurement valuecorresponding to a chamber condition. In some embodiments, processinglogic can identify a portion of the normalized spectral data for aparticular wavelength that is associated with the particular type ofmetrology measurement. For example, processing logic can identify thatthe normalized spectral data for wavelength Y between an initial timeperiod and a first intermediate time period is associated with acritical dimension measurement. A normalization may include processingthe incoming sensor data (e.g., spectral data and/or chamber data) toappear similar across the various chambers and sensors used to acquirethe data.

At block 714, processing logic determines if there is an anomaly in thereceived in-situ data collected in association with a substrate process.An anomaly may include identifying one or more datapoints having acombination of values (e.g., a spectral value, a chamber parametervalue, etc.) that don’t correlate with historical combinations of theassociation value. Processing logic may determine that the received datameets a threshold condition (e.g., threshold specification). A thresholdcondition may include the data matching (e.g., correlating) withhistorical data (e.g., having a threshold condition above a correlationcoefficient). Processing logic may determine the absence of a processinganomaly and proceeds along the no path to block 720 and continue toprocess the received data further. Processing logic may determine thepresence of an anomaly within the received data sets, and proceed alongthe yes branch to block 716.

In some embodiments, processing logic inputs the received data (e.g.,sensor data, spectral data, chamber data) into a model (e.g., astatistical model). Processing logic receives one or more outputs fromthe model. The one or more outputs may indicate a level of confidencethat temporally associated data points of the chamber data, spectraldata, and/or process result data accurately indicate conditions of theprocessing chamber. The statistical model may be generated using aregression between historical chamber data, historical spectral data,and/or historical process result data. Processing logic may determinethat the level of confidence meets a threshold decisions to determinethe presence or absence of an anomaly within the received data.

In some embodiments the statistical model is generated using statisticalprocess control (SPC) analysis to determine control limits for thereceived data and identify data as being more or less dependable basedon those control limits. In some embodiments, the statistical model isassociated with univariate and/or multivariate data analysis (e.g., ofthe historical data). For example, various parameters can be analyzedusing the statistical model to determine patterns and correlationsthrough statistical processes (e.g., range, minimum, maximum, quartiles,variance, standard deviation, and so on). In another example,relationships between multiple variables (e.g., chamber data andspectral data) can be ascertained using regression analysis, pathanalysis, factor analysis, multivariate statistical process control(MCSPC) and/or multivariate analysis of variance (MANOVA).

In some embodiments, processing logic determines data based on whether asubstrate criterion is satisfied. A substrate criterion may include oneor more of film thickness requirements, process uniformity requirements,critical dimension specification, SWA specification, etc.) In someembodiments, processing logic can determine that a substrate criterionis satisfied by determining that the received spectral data correspondsto spectral data associated with a particular type of substrate processand/or the particular type of substrate. For example, processing logiccan retrieve (e.g., from data store 150) spectral data that waspreviously collected for a particular type of substrate that wasprocessed according to a particular type of process. The previouslycollected spectral data can include one or more spectral data features(e.g., a spectral signature) that are specific to the type of substrateand/or the type of process, or a particular step or time period of thetype of process. Processing logic can determine whether the receivedspectral data for the current substrate includes one or more spectraldata features that correspond to (i.e., approximately equal) therespective spectral data features that are included in the previouslycollected spectral data.

At block 716, corrective action is performed. In some embodiments,chamber examination is carried out to identify one or more deficienciesof the manufacturing equipment and determines a corrective action toremedy the deficiency. Processing logic may cause a notification to bedisplayed on a GUI indicating an error, a data anomaly, and/or acorrective action to be taken, etc. In some embodiments, processinglogic initiates the creation, training, validation, and/or selection ofone or more new models to perform the actions described herein. In someembodiments, processing logic may update the currently used model tocorrect for the detected anomalies. For example a user may load an oldmodel that specifies the type of mode, the settings/parameters, and olddata used to create the model. Processing logic may use the old model asa starting place (e.g., initial guess) for training an updated model.Additional data may be added to the system and used to further trainand/or validate the model to generate an updated model. Processing logicproceed with updating new data and further evaluating the system afterperforming the corrective action with the new data.

At block 720, processing logic provides the sensor data to be used asinput to a trained machine learning model. In some embodiments,processing logic can provide the spectral data to predictive engine 328,which can cause predictive engine 328 to perform one or more operationsof method 800 described with respect to FIG. 8 .

At block 722, processing logic obtains a chamber condition metricextracted from one or more outputs of the trained machine learningmodel. In some embodiments, processing logic can receive an indicationof the chamber condition metric from predictive engine 328, obtained inaccordance with method 800. At block 724, processing logic determineswhether a chamber condition metric criterion is satisfied. In someembodiments, processing logic can determine whether the chambercondition metric criterion is satisfied by determining whether thesubstrate processed within the chamber under the current chamberconditions meets a process result criterion associated with the currentprocess. The extracted chamber condition metric can correspond to arecovery status of a processing chamber associated with a recoveryprocess (e.g., chamber seasoning) subsequent to a PM procedure.

In response to processing logic determining the chamber condition metriccriterion is satisfied, method 700 proceeds to block 728. At block 728,processing logic continues the current process for the currentsubstrate. In some embodiments, processing logic can continue thecurrent process by transmitting an instruction to system controller 228or the local controller for the process chamber to continue the currentprocess. In other or similar embodiments, processing logic can continuethe current process by generating and transmitting no instruction(s).Processing logic can transmit the sensor data (e.g., spectral data,chamber data) and the extracted metrology measurement value to trainingset generator 324 to be used as additional training data, as describedabove.

In response to processing logic determining that the chamber conditionmetric criterion is not satisfied, method 700 proceeds to block 726. Atblock 726, processing logic generates an instruction to alter aperformance of the manufacturing equipment. In some embodimentsprocessing logic transmits an instruction to terminate the currentprocess at the manufacturing system. In some embodiments, processinglogic can transmit the instruction to the system controller (e.g.,system controller 228), which causes the system controller to terminatethe current process. In other or similar embodiments, processing logiccan transmit the instruction to a local processing device for theprocess chamber, which causes the process chamber to terminate thecurrent process. In additional or alternative embodiments, processinglogic can transmit the spectral data and the extracted metrologymeasurement value to training set generator 324 (i.e., at server machine320 or at server machine 350) to be used as additional training data. Insome embodiments, processing logic transmits an instruction to updateone or more process parameters associated with the manufacturingequipment. The instruction may further include a command to furtherprocess the current substrate according to the updated processparameters.

Referring back to block 724, in some embodiments, processing logic canfurther determine whether the chamber condition metric criterion issatisfied by determining whether the chamber condition metric fallswithin a range of expected chamber condition metric values associatedwith the time period for the current process. The range of expectedchamber condition metrics can include a set of values that are expectedto be associated with the current substrate at the current time periodof the current process. In some embodiments, processing logic candetermine, in view of the extracted chamber condition metric value(s),that an accuracy of the trained machine learning model no longersatisfies an accuracy criterion (i.e., an overall accuracy of thetrained machine learning model falls below a threshold overallaccuracy). Inline or integrated metrology data can be employed to verifythe accuracy of the trained machine learning model. For example, themodel may output that the chamber is operating under normal operatingcondition however the metrology data associated processed under thesupposed normal operating conditions may fail to meet process resultrequirements (e.g., thickness requirements, critical dimensionrequirements, SWA requirements, etc.) Accordingly, processing logic cantransmit a notification to the client device indicating that the machinelearning model is to be retrained. In some embodiments, processing logiccan transmit a notification to training set generator 324 and/ortraining engine 326 to re-train the machine learning model, inaccordance with embodiments described herein.

At block 730, processing logic proceeds to the next iteration ofmeasurements. The substrate and/or process recipe and performs method700 using data associated with further processing a current substrateand/or processing the next substrate and/or using the next processrecipe.

FIG. 8 is a flow chart of a method 800 for predicting chamber conditionsof a processing chamber processing a current chamber using a machinelearning model, according to aspects of the present disclosure. Method800 is performed by processing logic that can include hardware(circuitry, dedicated logic, etc.), software (such as is run on ageneral purpose computer system or a dedicated machine), firmware, orsome combination thereof. In one implementation, method 800 can beperformed by a computer system, such as system architecture 100 of FIG.1 . In other or similar implementations, one or more operations ofmethod 800 can be performed by one or more other machines not depictedin the figures. In some aspects, one or more operations of method 800can be performed by predictive engine 328 of server machine 320described with respect to FIG. 3 .

At block 810, processing logic receives spectral data and chamber dataassociated with a processing chamber processing a substrate. In someembodiments, the spectral data can include optical emission spectra(OES) measurements of a plasma disposed within the processing chamber.In some embodiments, the spectral data can include optical reflectancespectra measurements corresponding to a reflectance pattern of lightreflected off a surface of the substrate disposed within the processingchamber. In some embodiments the spectra and chamber data can bereceived from chamber status equipment, such as chamber status equipment124, or other sensors disposed within or coupled to the process chamber,in accordance with previously described embodiments.

At block 812, processing logic generates normalized sensor that includesan indication of a spectral feature. Processing logic may performfunctions and/or methodology to extract feature and/or generatesynthetic/engineered data associated with the received sensor data. Forexample, processing logic may perform a feature extraction of thespectral data to identify spectral features. In another example,processing logic may perform a feature extraction using combinations ofspectral data to determine whether a criterion is satisfied. Processinglogic can analyze multiple data point of an associated parameter todetermine whether rapid changes occurred during a portion of a substrateprocess. In some embodiments, processing logic perform a normalizationacross the various sensor data (e.g., spectral data and/or chamber data)associated with various process chamber conditions. A normalization mayinclude processing the incoming sensor data (e.g., spectral data and/orchamber data) to appear similar across the various chambers and sensorsused to acquire the data.

At block 814, processing logic provides the normalized sensor data asinput to the trained machine learning model. In some embodiments, thetrained machine learning model can correspond to machine learning model334, described with respect to FIG. 3 . In some embodiments, trainingengine 326 selects machine learning model 334 for use by predictiveengine 328, in accordance with embodiments described with respect toFIG. 9 below. At block 816, processing logic obtains one or more outputsof the machine learning model. At block 818, processing logic extracts,from the one or more outputs, chamber condition metrics identifying: (i)one or more chamber conditions associated with the sensor data, and (2)an indication of a level of confidence that each of the one or morechamber conditions corresponds to a condition of the processing chamber.In one example, the level of confidence is a real number between 0 and 1inclusive. It should be noted that, in some embodiments, the level ofconfidence does not correspond to a probability. For example, the sum ofthe confidence levels for all chamber condition metrics may not equal 1.

In some embodiments, processing logic can use the chamber conditionmetrics to determine a recovery status of a processing chambercorresponding to a recovery procedure performed subsequent to apreventative maintenance (PM) procedure being performed. In someembodiments, if the level of confidence for the chamber conditionmetrics satisfy a threshold condition, then a processing condition ofthe processing chamber is identified as being associated with thechamber condition metrics. Processing logic can determine that the levelof confidence for the metrology measurement value satisfies thethreshold condition in response to determining that the level ofconfidence exceeds a threshold level of confidence. Processing logic canprovide the chamber condition metrics to chamber condition engine 330,in accordance with embodiments described with respect to FIG. 10 .

FIG. 9 is a flow chart of a method 900 for selecting a machine learningmodel for estimating a type of metrology measurement value, according toaspects of the present disclosure. Method 900 is performed by processinglogic that can include hardware (circuitry, dedicated logic, etc.),software (such as is run on a general purpose computer system or adedicated machine), firmware, or some combination thereof. In oneimplementation, method 900 can be performed by a computer system, suchas system architecture 100 of FIG. 1 . In other or similarimplementations, one or more operations of method 900 can be performedby one or more other machines not depicted in the figures. In someaspects, one or more operations of method 900 can be performed bytraining engine 326 of server machines 320 or 350, described withrespect to FIG. 3 .

At block 910, processing logic receives training data and/or validationdata for a set of machine learning models. Each of the set of machinelearning models can correspond to a different machine learning modeltype. For example, each of the set of machine learning models cancorrespond to a linear regression model, a partial least squaresregression model, a Gaussian regression model, a random forest model, asupport vector machine model, a neural network, a ridge regressionmodel, and so forth. In some embodiments, processing logic can receivetraining data and the validation data from training set generator 324 ofserver machines 320 or 350. Training set generator 324 can generatetraining data in accordance with embodiments described with respect tomethod 400 of FIG. 4 . In other or similar embodiments, processing logiccan receive the training data from training set generator 324 and cangenerate the validation data based on the received training data.Training data can correspond to training set 340 and validation data cancorrespond to data included in validation set 342, described withrespect to FIG. 3 .

At block 912, processing logic trains each of the set of machinelearning models using the received training data. At block 914,processing logic performs one or more testing operations for each of theset of machine learning models using the validation data. As describedabove, validation set 342 can include sensor data (e.g., chamber dataand spectral data) and chamber conditions corresponding to metrologydata for a processing chamber processing a prior substrate that isdifferent from sensor data (e.g., spectral data and chamber data) andchamber condition corresponding to metrology data included the trainingdata. To perform the one or more testing operations, processing logiccan provide the sensor data of validation set 342 as input to each ofthe set of trained machine learning models and can obtain one or moreoutputs of each of the trained models. Processing logic can extract achamber condition metric from the obtained one or more outputs, inaccordance with embodiments provided herein.

In some embodiments the trained machine learning model is to receive anew input having new sensor data having new chamber data indicating anew state of a new environment of a new processing chamber processing anew substrate according to a new process and new spectral dataindicating optical emission spectra (OES) measurements of a new plasmadisposed within the new processing chamber processing the new substrateaccording to the new process to produce a new output based on the newinput. The new output indicates a chamber condition metric correspondingto a recovery status associated with a chamber recovery processperformed subsequent to a preventative maintenance procedure.

At block 916, processing logic assigns a performance rating to each ofthe set of machine learning models based on an outcome of the one ormore testing operations performed with respect to block 914. In someembodiments, processing logic can assign a performance rating to arespective machine learning model based on an accuracy score determinedfor the machine learning model. Processing logic can determine anaccuracy score based on a difference between a chamber condition metricextracted from output(s) of the respective machine learning model and anactual chamber condition for a processing chamber processing. Forexample, as described with respect to block 914, processing logic canprovide sensor data from validation set 342 as input to a respectivemachine learning model and can extract a chamber condition metric froman output of the model. Processing logic can compare the extractedchamber condition metric with an actual chamber condition (e.g., bycomparing metrology results of wafer processing under both conditions)with the provided sensor data of the validation set 342. Processinglogic can assign an accuracy score to the model based on the differencebetween the extracted value and the actual value of validation set 342.For example, processing logic can assign a high accuracy score to arespective model if the difference between the extracted value producedby the model and the actual value is small. Similarly, processing logiccan assign a low accuracy score to the respective model if thedifference is large.

In additional or alternative embodiments, processing logic can furtherassign the performance rating to the respective machine learning modelbased on a speed score determined for the machine learning model. Insome embodiments, processing logic can determine the speed score basedon an amount of time after the processing logic provides the sensor dataas input to the model that the one or more outputs of the model areobtained. In other or similar embodiments, processing logic candetermine the speed score based on the amount of time after theprocessing logic provides the spectral data as input to the model thatthe processing logic extracts the metrology measurement value from theone or more obtained outputs. In one example, the processing logic canassign a high speed score to a respective model if the amount of timeafter the processing logic provides the sensor data as input to themodel that the model provides the one or more outputs (or the processinglogic extracts the metrology measurement value) is small.

In some embodiments, processing logic can assign the performance ratingto the respective machine learning model based on an efficiency scoredetermined for the machine learning model. As described with respect toFIG. 3 , in some embodiments, training engine 356 can be included atserver machine 350, which is separate from server machine 320. In suchembodiments, training engine 356 can train each of the set of themachine learning models at server machine 350. Training engine 356 canperform the one or more testing operations described with respect toblock 914 to determine which of the set of machine learning models areto be transmitted to server machine 320 for use by predictive engine328. Processing logic can determine the efficiency score for arespective machine learning model based on an overall system efficiency(e.g., for the manufacturing system) with respect to transmitting therespective machine learning model from server machine 350 to servermachine 320 and/or initializing the respective machine learning model atserver machine 320. In some embodiments, processing logic can determinethe efficiency score based on an amount of memory used to store therespective trained machine learning model, the amount of memoryavailable at data store 332 of server machine 320, the amount of networkbandwidth available to transmit the respective trained machine learningmodel to server machine 320, and so forth.

At block 918, processing logic determines whether a performancecriterion is satisfied based on the assigned performance rating for eachof machine learning models. In some embodiments, processing logic candetermine whether the performance criterion is satisfied by determiningwhether the assigned performance rating (i.e., determined based on theaccuracy score and, in some embodiments, the speed score and/or theefficiency score) for a respective machine learning model exceeds athreshold performance score. In other or similar embodiments, processinglogic can determine whether the performance criterion is satisfied bydetermining whether the accuracy score for the respective model exceedsa threshold score and whether the overall performance rating (i.e.,determined based on the accuracy score and the speed score and/or theefficiency score) for the respective model exceeds a thresholdperformance score. In some embodiments, more than one trained machinelearning model can be associated with an accuracy score and/or aperformance rating that satisfies a threshold score and/or thresholdrating, respectively. In such embodiments, processing logic candetermine that the respective model of the more than one trained modelsthat is associated with the highest accuracy score and/or performancerating that satisfies the performance criterion. In some embodiments,processing logic can determine that no trained machine learning modelsof the set of machine learning models are associated with an accuracyscore and/or performance rating that satisfies the threshold scoreand/or threshold rating, respectively. In such embodiments, processinglogic can determine that no model satisfies the performance criterion.

In response to determining that the performance criterion is satisfied,processing logic proceeds to block 920. At block 920, processing logicselects a respective machine learning model to be applied to futuresensor data collected for future substrates processed according to afuture substrate process. As described above, in some embodiments,training engine 326 can be included with predictive engine 328 at servermachine 320. In such embodiments, in response to selecting therespective machine learning model to be applied to future spectral data,processing logic can store the respective model and/or an indicationthat the respective model is to be used by predictive engine 328 at datastore 332. In other embodiments, training engine 326 can be included atserver machine 350. In such embodiments, in response to training engine326 selecting the respective machine learning model to be applied tofuture sensor data, server machine 350 can transmit the respectivemachine learning model to server 320 for storage at data store 150.

In response to determining that the performance criterion is notsatisfied, processing logic proceeds to block 922. At block 922,processing logic receives additional training data for further trainingof the set of machine learning models. Processing logic can receive theadditional training data from training set generator 324, in accordancewith previously described embodiments. In the case of performancecriteria is not satisfied, after data is received by block 922, thesystem may send the data to block 910 and start the flow of method 900through another iteration.

FIG. 10 depicts a diagrammatic representation of a machine in theexample form of a computing device 1000 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, can be executed. In alternativeembodiments, the machine can be connected (e.g., networked) to othermachines in a Local Area Network (LAN), an intranet, an extranet, or theInternet. The machine can operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine can be a personal computer (PC), a tablet computer, a set-topbox (STB), a Personal Digital Assistant (PDA), a cellular telephone, aweb appliance, a server, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines (e.g., computers)that individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein. In embodiments, computing device 1000 can correspond to one ormore of server machine 170, server machine 180, predictive server 112,system controller 228, server machine 320, or server machine 350, asdescribed herein.

The example computing device 1000 includes a processing device 1002, amain memory 1004 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), astatic memory 1006 (e.g., flash memory, static random access memory(SRAM), etc.), and a secondary memory (e.g., a data storage device1028), which communicate with each other via a bus 1008.

Processing device 1002 can represent one or more general-purposeprocessors such as a microprocessor, central processing unit, or thelike. More particularly, the processing device 1002 can be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 1002 can also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. Processing device 1002 can also be orinclude a system on a chip (SoC), programmable logic controller (PLC),or other type of processing device. Processing device 1002 is configuredto execute the processing logic for performing operations and stepsdiscussed herein.

The computing device 1000 can further include a network interface device1022 for communicating with a network 1064. The computing device 1000also can include a video display unit 1010 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1012 (e.g., a keyboard), a cursor control device 1014 (e.g., a mouse),and a signal generation device 1020 (e.g., a speaker).

The data storage device 1028 can include a machine-readable storagemedium (or more specifically a non-transitory computer-readable storagemedium) 1024 on which is stored one or more sets of instructions 1026embodying any one or more of the methodologies or functions describedherein. Wherein a non-transitory storage medium refers to a storagemedium other than a carrier wave. The instructions 1026 can also reside,completely or at least partially, within the main memory 1004 and/orwithin the processing device 1002 during execution thereof by thecomputer device 1000, the main memory 1004 and the processing device1002 also constituting computer-readable storage media.

While the computer-readable storage medium 1024 is shown in an exampleembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth in orderto provide a good understanding of several embodiments of the presentdisclosure. It will be apparent to one skilled in the art, however, thatat least some embodiments of the present disclosure can be practicedwithout these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular implementations can vary from these exemplarydetails and still be contemplated to be within the scope of the presentdisclosure.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” When the term “about” or “approximately” is usedherein, this is intended to mean that the nominal value presented isprecise within ± 10%.

Although the operations of the methods herein are shown and described ina particular order, the order of operations of each method can bealtered so that certain operations can be performed in an inverse orderso that certain operations can be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations can be in an intermittentand/or alternating manner.

It is understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

1. A method, comprising: receiving, by a processing device, sensor datacomprising (i) chamber data indicating a state of an environment of aprocessing chamber processing a substrate according to a set of processparameters of a current process and (ii) spectral data indicatingoptical emission spectra (OES) measurements of a plasma disposed withinthe processing chamber processing the substrate according to the set ofprocess parameters of the current process; using, by the processingdevice, the sensor data as input to a machine learning model; obtaining,by the processing device, one or more outputs of the machine learningmodel, the one or more outputs indicating one or more chamber conditionmetrics; determining, by the processing device, a recovery status of theprocessing chamber based on the one or more chamber condition metrics,the recovery status corresponding to a chamber recovery processperformed subsequent to a preventative maintenance procedure; andcausing, by the processing device, a modification to a performance ofthe processing chamber based on the recovery status of the processingchamber.
 2. The method of claim 1, further comprising: determining, bythe processing device, an update to at least one process parameter ofthe set of process parameters to generate an updated set of processparameters based on the one or more chamber condition metrics, whereinthe modification to the performance of the processing chamber is furtherbased on the update to the at least one process parameter of the set ofprocess parameters.
 3. The method of claim 2, wherein causing themodification to the performance of the processing chamber furthercomprises sending a first command that causes at least one of: thesubstrate or a new substrate to be processed according to the updatedset of process parameters; or substrate processing within the processingchamber to halt.
 4. The method of claim 1, further comprising causing anotification to be displayed on a graphical user interface (GUI), thenotification indicating the modification to the performance of theprocessing chamber.
 5. The method of claim 1, wherein the modificationto the performance corresponds to a chamber seasoning procedure.
 6. Themethod of claim 1, wherein the spectral data further comprises: opticalreflectance spectra measurements corresponding to a reflectance patternof light reflected off a surface of the substrate disposed within theprocessing chamber.
 7. The method of claim 6, further comprising:determining one or more spectral features based on combinations of theoptical emission spectra measurements and the optical reflectancespectra measurements to generate feature data; and using the featuredata as input to the machine learning model.
 8. The method of claim 1,further comprising: using the sensor data as input to a statisticalmodel; receiving one or more outputs from the statistical model, the oneor more outputs indicating a level of confidence that temporallyassociated datapoints of the chamber data and the spectral dataaccurately indicate conditions of the processing chamber, wherein thestatistical model is generated using a regression between historicalchamber data and historical spectral data; and determining that thelevel of confidence is meets a threshold condition.
 9. The method ofclaim 1, wherein receiving the sensor data and causing the modificationto the performance of the processing chamber both occur while theprocessing chamber is processing the substrate according to the set ofprocess parameters.
 10. A method for training a machine learning modelto determine a status of a processing chamber in a chamber recoveryprocedure, the processing chamber processing a current substrateaccording to a current process, the method comprising: generatingtraining data for the machine learning model, wherein generating thetraining data comprises: identifying a first training input havinghistorical sensor data comprising i) historical chamber data indicatinga state of an environment of a second processing chamber processing aprior substrate according to a prior process and ii) historical spectraldata indicating optical emission spectra (OES) measurements of a priorplasma disposed within the second processing chamber processing theprior substrate according to the prior process; identifying a firsttarget output for the first training input, wherein the first targetoutput comprises historical process result data having process resultmeasurements of the prior substrate processed using the secondprocessing chamber according to the prior process; and providing thetraining data to train the machine learning model on (i) a set oftraining inputs comprising the first training input and (ii) a set oftarget outputs comprising the first target output, wherein the trainedmachine learning model is to receive a new input having new sensor datacomprising i) new chamber data indicating a new state of a newenvironment of a new processing chamber processing a new substrateaccording to a new process and ii) new spectral data indicating opticalemission spectra (OES) measurements of a new plasma disposed within thenew processing chamber processing the new substrate according to the newprocess to produce a new output based on the new input, the new outputindicating a chamber condition metric corresponding to a recovery statusassociated with a chamber recovery process performed subsequent to apreventative maintenance procedure.
 11. The method of claim 10, whereinthe historical spectral data further comprises: optical reflectancespectra measurements corresponding to a reflectance pattern of lightreflected off a surface of the prior substrate disposed within thesecond processing chamber.
 12. The method of claim 11, furthercomprising: determining one or more spectral features based oncombinations of optical emission spectra measurements and opticalreflectance spectra measurements to generate feature data, the trainingdata further comprising the feature data.
 13. The method of claim 10,further comprising: performing a data extrapolation procedure with thehistorical spectral data to generate optical emission spectra (OES)estimations corresponding to instances of time occurring before or afterthe OES measurements.
 14. The method of claim 10, further comprising:performing a data interpolation procedure with the historical spectraldata to generate optical emission spectra (OES) estimations of one ormore instance of times occurring between pairs of the OES measurements.15. The method of claim 10, wherein each training input in the set oftraining inputs is mapped to a target output in the set of targetoutputs.
 16. The method of claim 10, wherein the trained machinelearning model comprises at least one of a logistic regression typealgorithm, a multi-layer perception algorithm, or a recurrent neuralnetwork (RNN).
 17. A non-transitory computer readable medium comprisinginstructions that, when executed by a processing device, cause theprocessing device to: receive sensor data comprising (i) chamber dataindicating a state of an environment of a processing chamber processinga substrate according to a set of process parameters of a currentprocess and (ii) spectral data indicating optical emission spectra (OES)measurements of a plasma disposed within the processing chamberprocessing the substrate according to the set of process parameters ofthe current process; use the sensor data as input to a machine learningmodel; obtain one or more outputs of the machine learning model, the oneor more outputs indicating one or more chamber condition metrics;determine a recovery status of the processing chamber based on the oneor more chamber condition metrics, the recovery status corresponding toa chamber recovery process performed subsequent to a preventativemaintenance procedure; and cause a modification to a performance of theprocessing chamber based on the recovery status of the processingchamber.
 18. The non-transitory computer readable medium of claim 17,wherein the instructions, when executed by the processing device furthercauses the processing device to: determine an update to at least one ofthe set of process parameters to generate an updated set of processparameters based on the one or more chamber condition metrics, whereinthe modification to the performance of the processing chamber is furtherbased on the update to the at least one of the set of processparameters.
 19. The non-transitory computer readable medium of claim 17,wherein the modification to the performance corresponds to a chamberseasoning procedure.
 20. The non-transitory computer readable medium ofclaim 17, wherein the instructions, when executed by the processingdevice further causes the processing device to: use the sensor data asinput to a statistical model; receive one or more outputs from thestatistical model, the one or more outputs indicating a level ofconfidence that temporally associated data points of the chamber dataand the spectral data accurately indicate conditions of the processingchamber, wherein the statistical model is generated using a regressionbetween historical chamber data and historical spectral data; anddetermine that the level of confidence is meets a threshold condition.